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Chronic Disease Prevention and Control in Austin/Travis County. Denton Cardiovascular Health Summit August 26, 2010 . Agenda:. Burden of Chronic Disease in Austin/Travis County Behavioral Risk Factor Data What we are doing to address chronic disease

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chronic disease prevention and control in austin travis county

Chronic Disease Prevention and Control in Austin/Travis County

Denton Cardiovascular Health Summit

August 26, 2010

agenda
Agenda:
  • Burden of Chronic Disease in Austin/Travis County
  • Behavioral Risk Factor Data
  • What we are doing to address chronic disease
  • PRISM Modeling for priority setting and projecting impact of proposed interventions
burden of chronic diseases
Burden of Chronic Diseases
  • Leading causes of disability and death in Texas and in the United States
  • Account for 3 out of every 4 deaths
  • Cause major limitations in daily living for more than 1 of every 10 Americans
  • Chronic diseases account for 1/3 of the years of potential life lost before age 65
  • Account for 75% of the $1 trillion spent on health care each year in the United States
slide4
The leading causes of death have changed in this century from infectious diseases to chronic diseases

In 1900, pneumonia and influenza, TB, and gastritis, enteritis, and colitis were the 3 leading causes of death accounting for nearly 1/3 of all deaths.

Today, heart disease, cancer and stroke are the 3 leading causes of death accounting for almost 2/3 of all deaths.

slide5

Leading Causes of Death, Travis County, 2007

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://soupfin.tdh.state.tx.us/death10.htm

slide6

Diseases of the Heart1 Age Adjusted Mortality Rate Travis County 1999-2006

1 ICD 9 Codes: I00-I09, I11, I13, I20-I51

Data Source: Texas Department of State Health Services, Center for Health Statistics http://soupfin.tdh.state.tx.us/death10.htm

slide7

Age-Adjusted Heart Disease Mortality Rate

by Zip Code of Residence

Travis County, Texas

This map has been produced by the Austin/Travis County Health & Human Services Department as a working staff map. No warranty is made by A/TCHHSD regarding its accuracy or completeness.

2001-2004

Rates per 100,000

cancer death rates for men us 1930 1999
Cancer Death Rates*, for Men, US, 1930-1999

Rate Per 100,000

Lung

Prostate

Stomach

Colon and rectum

Pancreas

Leukemia

Liver

*Age-adjusted to the 2000 US standard population.

Source: US Mortality Public Use Data Tapes 1960-1999, US Mortality Volumes 1930-1959,

National Center for Health Statistics, Centers for Disease Control and Prevention, 2002.

cancer death rates for women us 1930 1999
Cancer Death Rates*, for Women, US, 1930-1999

Rate Per 100,000

Lung

Uterus

Breast

Colon and rectum

Stomach

Ovary

Pancreas

*Age-adjusted to the 2000 US standard population.

Source: US Mortality Public Use Data Tapes 1960-1999, US Mortality Volumes 1930-1959,

National Center for Health Statistics, Centers for Disease Control and Prevention, 2002.

slide10

Lung and Bronchus Cancer1 Age Adjusted Mortality Rate Travis County 1999-2006

1 ICD 9 Codes: C34

* The number of deaths due to Lung and Bronchus Cancer for Hispanics is too small for rate calculation in 2001 and 2003.

Data Source: Texas Department of State Health Services, Texas Cancer Registry, http://www.cancer-rates.info/tx/

slide11

Age-Adjusted Lung Cancer Mortality Rate

by Zip Code of Residence

Travis County, Texas

This map has been produced by the Austin/Travis County Health & Human Services Department as a working staff map. No warranty is made by A/TCHHSD regarding its accuracy or completeness.

2001-2004

Rates per 100,000

slide12

Diabetes Mellitus1 Age Adjusted Mortality Rate Travis County 1999-2006

1 ICD 9 Codes:E10-E14

The number of deaths due to Diabetes Mellitus for Black Non-Hispanics is too small for rate calculation in 1999, 2005, and 2006.

Data Source: Texas Department of State Health Services, Center for Health Statistics, http://soupfin.tdh.state.tx.us/death10.htm

slide13

Age-Adjusted Diabetes Mortality Rate

by Zip Code of Residence

Travis County, Texas

This map has been produced by the Austin/Travis County Health & Human Services Department as a working staff map. No warranty is made by A/TCHHSD regarding its accuracy or completeness.

2001-2004

Rates per 100,000

behavioral risk factors and disease prevalence from brfss
Behavioral Risk Factors and Disease Prevalence From BRFSS

Cigarette Smoking

Overweight/Obesity

Lack of Physical Activity

Poor Nutrition

Diabetes

slide15

Leading Causes of Death, Travis County, 2007

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://soupfin.tdh.state.tx.us/death10.htm

smoking attributable deaths travis county 2007
Smoking-Attributable DeathsTravis County, 2007

Approximately 584 deaths caused by smoking among adults 35+ years in Travis County (over 11 each week)

health effects of tobacco use
Health Effects of Tobacco Use
  • Cigarette smoking remains the leading preventable cause of death in the United States, causing an estimated 438,000 deaths - or about 1 out of every 5 - each year. (In 2007 in Travis County approximately 584 smoking-related deaths-over 11 per week)
  • Tobacco kills more than AIDS, heroin, cocaine, alcohol, car accidents, fire and murder – combined.
  • Lung cancer is the leading cause of cancer death among both men and women in the United States, with 85- 90 percent of lung cancer deaths attributed to smoking.
  • Smoking also increases the risk of many other types of cancer, including cancers of the throat, mouth, pancreas, kidney, bladder, and cervix.
  • People who smoke are up to six times more likely to suffer a heart attack than nonsmokers, and the risk increases with the number of cigarettes smoked. Smoking also causes most cases of chronic obstructive lung disease, which includes bronchitis and emphysema.
  • In the United States, approximately 38,000 deaths each year are caused by exposure to secondhand smoke.
second hand smoke shs
Second-Hand Smoke (SHS)
  • Public Health Issue
    • Contains over 4,000 chemicals including 69 known carcinogens
    • Group A Carcinogen (like asbestos and benzene)
    • Health Effects: Adults
      • Lung cancer
      • Coronary heart disease
    • Health Effects: Children
      • Lower respiratory tract infections in children < 18 months old each year
      • Middle ear infections
      • Asthma
      • SIDS
even brief exposure to shs poses serious health risks
Even Brief Exposure to SHS Poses Serious Health Risks
  • Even brief exposure to SHS can:
    • result in upper airway changes in healthy persons
    • lead to more frequent asthma attacks in asthmatic children
  • Just 30 minutes of exposure to SHS can:
    • increase risk of blot clots
    • slow the rate of blood flow through the coronary arteries
    • injure blood vessels and interfere with their repair
prevalence of cigarette smoking among youth 1 texas public schools 2001 2005 2007 2009
Prevalence of Cigarette Smoking Among Youth1, Texas’ Public Schools 2001, 2005, 2007, 2009

1Youth is defined as a student in public schools ranging in age from 15-18 years old.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/yrbs/query/yrbss_form.shtm

slide25

Prevalence of Cigarette Smoking Among Youth1, STEPS to a Healthier Austin Intervention Area’s Public Schools 2007

Percentage of students who smoked cigarettes on one or more of the past 30 days

100

80

60

40

19.4

20.5

13.8

14.0

20

11.1

12.5

9.6

7.2

9.1

10.7

0

Total

Male

Female

9th

10th

11th

12th

Black*

Hispanic/

White*

Latino

1Youth is defined as a student in public schools ranging in age from 15-18 years old.

Data Source: Centers for Disease Control and Prevention, Youth Risk Behavior Survey

QN30 - Weighted Data

*Non-Hispanic.

prevalence of overweight and obesity 1 among adults 2 2002 2008
Prevalence of Overweight and Obesity1 among Adults2 2002-2008

1BMI Overweight and Obsesity ≥25

2Adults are defined as respondents 18 years old and older.

Data Source: Texas Department of State Health Services, Center for Health Statistics, http://www.dshs.state.tx.us/chs/brfss/query/brfss_form.shtm; Austin/Travis County Health & Human Services Department

slide28

Prevalence of Overweight Youth1 in Texas’ Public Schools 2001, 2005, 2007, and 2009

1Youth is defined as a student in public schools ranging in age from 15-18 years old.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/yrbs/query/yrbss_form.shtm

prevalence of lack of physical activity 1 among adults 2 2002 2008
Prevalence of Lack of Physical Activity1 Among Adults2, 2002-2008

1Lack of activity is defined as respondents who report no leisure-time physical activity during the past month.

2Adults are defined as respondents 18 years old and older.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/brfss/query/brfss_form.shtm; Austin/Travis County Health & Human Services Department

prevalence of lack of physical activity 1 among adults 2 travis county 2008
Prevalence of Lack of Physical Activity1 Among Adults2Travis County 2008

1Lack of activity is defined as respondents 18 years and older who report no leisure-time physical activity during the past month.

2Adults are defined as respondents 18 years old and older.

In 2008, the sample sizes for Black non-Hispanics and 18-29 year olds were less than 50 respondents and consequently data could not be analyzed.

Data Source: Austin/Travis County Health & Human Services Department

prevalence of lack of physical activity 1 in adults 2 travis county 2008
Prevalence of Lack of Physical Activity1 in Adults2, Travis County, 2008

1Lack of activity is defined as respondents 18 years and older who report no leisure-time physical activity during the past month.

2Adults are defined as respondents 18 years old and older.

In 2008, the sample size for Less than High School was less than 50 respondents and consequently data could not be analyzed.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/brfss/query/brfss_form.shtm

prevalence of poor nutrition 1 in adults 2 austin round rock msa 2007
Prevalence of Poor Nutrition1in Adults2, Austin-Round Rock MSA, 2007

1Poor nutrition is defined as eating certain fruits, fruit juices, or vegetables less than 5 times per day.

2Adults are defined as respondents 18 years old and older.

In 2008, the sample size for Black non-Hispanics was less than 50 respondents and consequently data could not be analyzed.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/brfss/query/brfss_form.shtm

slide33
Prevalence of Eating 5 or More Fruits and Vegetables Per Day in Youth1 in Texas’ Public Schools, 2001, 2005, 2007, and 2009

1Youth is defined as a student in public schools ranging in age from 15-18 years old.

Data Source: Texas Department of State Health Services, Center for Health Statistics

http://www.dshs.state.tx.us/chs/yrbs/query/yrbss_form.shtm

prevalence of diabetes 1 in adults 2 united states texas travis county 2002 2008
Prevalence of Diabetes1 in Adults2, United States, Texas, Travis County 2002-2008

1Doctor diagnosed as having diabetes but not diagnosed while pregnant

2Adults are defined as respondents 18 years old and older.

Data Source: Texas Department of State Health Services, Center for Health Statistics, http://www.dshs.state.tx.us/chs/brfss/query/brfss_form.shtm (US, Texas)

Austin/Travis County Health and Human Services Department, (Travis County)

prevalence of diabetes 1 in adults 2 by sex race ethnicity and age group travis county 2008
Prevalence of Diabetes1 in Adults2 by Sex, Race/Ethnicity, and Age Group, Travis County - 2008

1Doctor diagnosed as having diabetes but not diagnosed while pregnant

2Adults are defined as respondents 18 years old and older.

Data Source: Austin/Travis County Health & Human Services Department

economic cost
Economic Cost
  • In 1998/1999, Texas Smoking-Attributable Costs = $10.09 billion
    • Direct Medical expenditures - $4.55 billion
    • Lost Productivity costs - $5.54 billion
  • In 1998, about 15% ($1,265,000,000 or $543.87 per recipient) of all Texas Medicaid expenditures were spent on smoking-related illnesses and diseases. (includes state and federal contributions to Medicaid)
economic costs due to smoking travis county 2007
Economic Costs Due To SmokingTravis County, 2007

584 deaths caused by smoking among adults 35+ years

  • Loss of future earnings and productivity of $165.4 million
  • 2004 Direct Health Care Costs for Travis County- $243 million
texas employer costs of smoking
Texas Employer Costs of Smoking

Estimated annual EXCESS absence and productivity cost per smoker $2,625 (not including health costs)

  • Smoke breaks $2,261
    • 26 minutes per day more than non-smokers (Source: Study by Halpern and Rentz) multiplied by the Texas average hourly wage $19.76 (Source: Bureau of Labor Statistics, May 2009)
  • Absences $364
    • 2.3 days of additional absences (Source: Study by Halpern and Rentz) multiplied by Texas average hourly wage of $19.76. (Source: Bureau of Labor Statistics, May 2009)

Tobacco-free environments reduces smoking prevalence by 3.8% and helps ex-smokers by eliminating cues to smoke and (Study by Fichtenberg and Glantz).

comprehensive smoke free workplace laws reduce cigarette consumption
Comprehensive smoke-free workplacelaws reduce cigarette consumption
  • “…total prohibition of smoking in the workplace strongly affects industry volume. Smokers facing these restrictions consume 11%-15% less than average and quit at a rate that is 85% higher than average...Milder workplace restrictions, such as smoking only in designated areas, have much less impact on quitting rates and very little effect on consumption.”
  • “…financial impact of smoking bans will be tremendous –three to five fewer cigarettes per day per smoker will reduce annual manufacturer profits a billion dollars plus per year.”

- Excerpts from Philip Morris internal documents

smokefree laws economic issues
Smokefree Laws:Economic Issues
  • The US EPA estimates that smokefree restaurants can expect to save about $190 per 1,000 square feet each year in lower cleaning and maintenance costs.
  • The National Fire Protection Association found that in 1998 smoking materials caused 8,700 fires in non-residential structures resulting in direct property damage of $60.5 million.
  • Landlords and restaurants with smokefree premises have negotiated lower fire and property insurance premiums.
tobacco industry expenditures
Tobacco Industry Expenditures
  • In 2006 the Tobacco Industry spent $12.49 billion on advertising and promotion in the U.S.
  • Approximately $1.01 billion was spent in Texas in 2006 (over $2.76 million every single day)
  • Approximately $41.75 million was spent in Travis County in 2006 ($114,400 every single day)
criteria for evaluating economic studies
Criteria for Evaluating Economic Studies
  • Based on objective data (i.e. sales tax)
  • Includes data for a sufficient time period before and after the ordinance
  • Accounts for underlying economic trends
  • Uses appropriate statistical methods
  • In peer-reviewed literature
  • Source of funding
texas economic studies methods
Texas Economic StudiesMethods
  • Quarterly data obtained from the Texas Comptroller’s Office
    • Taxable restaurant, bar and mixed beverage sales
    • Total retail sales
  • Linear regression model to assess the economic impact of clean indoor air ordinances
figure 1 gross restaurant bar and mixed beverage revenues by fiscal quarter el paso texas 1990 2002
Figure 1. Gross Restaurant, Bar and Mixed Beverage Revenues By Fiscal Quarter*—El Paso, Texas, 1990-2002

Smoking Ban in effect January 2, 2002

* First fiscal quarter of each year is January 1 –March 31

slide48
Figure 2. Restaurant, Bar and Mixed Beverage Revenues, Percent of Total Retail Revenues by Fiscal Quarter*—El Paso, Texas, 1990-2002

Smoking Ban in effect January 2, 2002

* First fiscal quarter of each year is January 1 –March 31

summary
Summary
  • 2003 study offered a comprehensive view of all available studies on the economic impact of smoke-free workplace laws (Over 97 studies, including 34 with smoke-free bars)
  • The study concluded that:

“All of the best designed studies report no impact or a positive impact of smoke-free restaurant and bar laws on sales or employment. Policymakers can act to protect workers and patrons from the toxins in secondhand smoke confident in rejecting industry claims that there will be an adverse economic impact.”

Scollo M, et al, Review of the quality of studies on the economic effects of smoke-free policies on the hospitality industry, Tobacco Control (2003); 12:13-20.

poor quality literature on smoke free bars and restaurants
Poor Quality Literature on Smoke-Free Bars and Restaurants
  • Supported by Tobacco Industry
  • Survey of bar owners on predicted impacts or anecdotal information
  • Bizarre time periods or inappropriate control groups for comparison
  • Non-peer reviewed
plausibility
Plausibility
  • In Texas there are over 4 times as many adult non-smokers as smokers
  • Prior experiences
    • Airline bans
    • Movie theatres
  • Texas Adult Survey
texas adult survey brfss 2009
Texas Adult Survey (BRFSS)2009
  • If there were a total ban on smoking in restaurants, would you eat out:
    • More often 27%
    • Less often 6%
    • No difference 67%
texas adult survey brfss 200956
Texas Adult Survey (BRFSS)2009
  • If there were a total ban on smoking in bars and music clubs, would you go to bars and music clubs:
    • More often 19%
    • Less often 8%
    • No difference 73%
slide57

“Back in 2002, when the City Council was weighing Mayor Michael R. Bloomberg’s proposal to eliminate smoking from all indoor public places, few opponents were more fiercely outspoken than James McBratney, president of the Staten Island Restaurant and Tavern Association.

He frequently ripped Mr. Bloomberg as a billionaire dictator with a prohibitionist streak that would undo small businesses like his bar and his restaurant. Visions of customers streaming to the legally smoke-filled pubs of New Jersey kept him awake at night.

Asked last week what he though of the now two-year-old ban, Mr. McBratney sounded changed. “I have to admit,” he said sheepishly, “I”ve seen no falloff in business in either establishment.” He went on to describe what he once considered unimaginable: Customers actually seem to like it and so does he.

New York Times Feb 6, 2005

key points
Key Points
  • International scientific consensus that SHS kills
  • SHS under typical conditions of smoker density and ventilation poses unacceptable risks to nonsmokers
  • SHS cannot be controlled to acceptable levels of risk by ventilation or air cleaning
  • No objective evidence to support the claim that smoke-free ordinances impose economic penalties on restaurant or bar owners
  • Restrictions on SHS are no different than any other restrictions to protect public health.
green and tobacco free
Green and Tobacco-Free
  • According to the Smithsonian Institute, "it takes ten years for one cigarette butt to degrade."
  • According to Eco Recycle, "50% of all litter in urban areas is tobacco-related products including butts, cellophane wrapping, foil inserts and packaging."
  • According to Cigarette Litter Org, "4.5 trillion cigarette butts are littered worldwide every year."
  • According to the Texas Department of Transportation: "130 million butts will be tossed out in Texas alone this year.“
  • Lower safety hazards
    • According to CleanUp.org.au, "a cigarette butt can smoulder for up to three hours causing a grass fire or even a bushfire."
    • According to a study by the Department of Epidemiology and Preventive Medicine at the University of California, "Smoking causes an estimated 30% of U.S. and 10% of global fire death burdens."
    • According to the Burn Prevention Committee of the American Burn Association, "Cigarettes are the most common ignition source for fatal house fires, which cause approximately 29% of the fire deaths in the United States."
chronic disease coalition
Chronic Disease Coalition?
  • Coordination
  • Networking
  • Resources and Tools
  • Policy and Environmental Changes

Chronic Disease Prevention

and Control Coalition

Disease/Risk Factor Coalitions and Work Groups

Diabetes

Cancer

Obesity

Prevention

Tobacco

Asthma

austin travis county cppw award
Austin/Travis County CPPW Award
  • Awarded $7.47 Million over 2 years to address tobacco
  • One of 44 communities selected (out of 263 applicants)
  • Changing the social norms
  • Extremely tight timelines
  • Expectation to perform
  • “The world is watching”
  • Implications for future funding
health care reform legislation
Health Care Reform Legislation

SEC. 4002 PREVENTION AND PUBLIC HEALTH FUND

(a) PURPOSE.—It is the purpose of this section to establish a Prevention and Public Health Fund (referred to in this section as the ‘‘Fund’’), to be administered through the Department of Health and Human Services, Office of the Secretary, to provide for expanded and sustained national investment in prevention and public health programs to improve health and help restrain the rate of growth in private and public sector health care costs.

(b) FUNDING.—There are hereby authorized to be appropriated to the Fund, out of any monies in the Treasury not otherwise appropriated—

(1) for fiscal year 2010, $500,000,000;

(2) for fiscal year 2011, $750,000,000;

(3) for fiscal year 2012, $1,000,000,000;

(4) for fiscal year 2013, $1,250,000,000;

(5) for fiscal year 2014, $1,500,000,000; and

(6) for fiscal year 2015, and each fiscal year thereafter, $2,000,000,000.

cppw strategies
CPPW Strategies
  • Emphasize high-impact, broad-reaching
    • policy,
    • environmental, and
    • systems changes
policy systems environmental change
Policy, Systems & Environmental Change

What they mean is, overcome the Stickiness Problem.

exercise participation effect of short bouts home treadmills jakicic et al jama 282 16
Exercise ParticipationEffect of Short Bouts, Home Treadmills(Jakicic et.al., JAMA 282, 16)

?

mark.fenton@verizon.net

exercise participation effect of short bouts home treadmills jakicic et al jama 282 1668
Exercise ParticipationEffect of Short Bouts, Home Treadmills(Jakicic et.al., JAMA 282, 16)

mark.fenton@verizon.net

self help vs commercial weight loss programs heshka et al jama 289 14 april 9 2003
Self-help vs. CommercialWeight Loss Programs(Heshka et.al., JAMA 289, 14; April 9, 2003)

mark.fenton@verizon.net

mapps strategies
MAPPS Strategies
  • Media,
  • Access,
  • Pricing,
  • Point of Purchase Prompts,
  • Social Support
media

By Loren Stein

CONSUMER HEALTH INTERACTIVE

Media
access tobacco free policies and laws
Access: Tobacco-Free Policies and Laws
  • Only way to protect non-smokers from secondhand smoke
  • Saves lives and prevents heart attacks (up to 17% average reduction in heart attack hospitalizations where smoke-free laws enacted)
  • Changes the social norm
  • Helps motivate smokers to quit
  • Worker safety issue - not “personal nuisance”

All workers deserve equal protection

  • Smoke-free workplace laws don’t hurt business
  • No trade-off between health and economics
access
Access
  • Tobacco-Free Worksite Policies
  • Support Tobacco-Free School and University Policies
  • Tobacco-Free Parks and Recreation
  • Disparities focus – Tobacco-Free policies with supporting cessation services for:

     Human service agencies

Integral Care,

Homeless Shelters 

Public Housing

  • Tobacco –Free Healthcare Facilities (with system changes to promote cessation)
  • Tobacco-Free Public Transit
early wins cap metro mhmr
Early Wins: Cap Metro/MHMR
  • Creates tobacco-free zones at all passenger boarding areas, for Park and Ride and transit stations
  • Tobacco-free metro rail platforms on September 1, 2010
  • Working towards tobacco-free bus stops
point of purchase
Point-Of-Purchase
  • Restrict point of purchase advertising
  • Labeling/ signage/ placement to discourage consumption of tobacco
social support
Social Support
  • Promote the Statewide Telephone Quitline (Including Free Nicotine Replacement)
  • Create a Community Cessation Resource Available for Referral
slide78

Cessation services save lives and are an important part of a comprehensive program

  • 70% of U.S. adult smokers report they want to quit
  • Quitting is hard, but most Americans who have ever smoked have already quit
  • Immediate health benefits to quitting, even in long-time smokers
  • Telephone counseling (quitlines) and low out-of-pocket costs for treatment increase uptake
  • Cessation services can double or triple quit rate –
dshs obesity prevention funding
DSHS obesity prevention funding

Goals:

  • Increase availability of affordable healthy food and beverages
  • Promote healthy food and beverage choices
  • Create safe communities that support physical activity
  • Promote physical activity/limiting sedentary activity among children and youth.

Phases:

  • Phase 1 (through Sept. 2010): Planning
    • Asset mapping, focus groups, work plan development
  • Phase 2 (through Sept. 2011): Implementation
partners
Partners:
  • WIC Program
  • Greater Austin Hispanic Chamber - Health & Wellness Committee
  • Equator Media Limited
  • Michael and Susan Dell Center for Advancement of Healthy Living –
  • Wye River Group on Healthcare
  • WRGH
  • Austin Independent School District
  • City of Austin HealthPLUS Employee Wellness Program
  • The Austin Project
  • Central Texas Community Health Centers d/b/a CommUnityCareAlliance for African American Health in Central Texas
  • Las Comadres para las Americas
  • Texas AgriLife Extension Service
  • Austin Area Yoga TeachersYouthLaunch
  • Bicycle Sport Shop
  • City of Austin/Travis Co. WIC Program,
  • Central Texas Healthy Mothers Healthy Babies
  • Seton Family of Hospitals
  • Garza's Gardens gonzalo garza ihs aisd
  • YouthLaunch, Inc.
  • Mothers' Milk
  • Austin Travis County Sustainable Food Policy Advisory Board
  • Sustainable Food Center
  • Austin Community College District (ACC),
  • Children's Optimal Health
  • Theatre Action Project
  • Del Valle Independent School District
  • Eanes Independent School District
  • LifeWorks
  • Austin Public LibraryLiteracy Coalition of Central Texas
  • Austin Travis County Integral Care (formerly MHMR)
  • Austin Child Guidance Center
  • Pflugerville ISD
  • Texas Round-Up, Inc.
  • YMCA of Austin
  • Safe Kids Austin, Led By Dell Children's Medical Center of Central Texas
  • Austin Cycling Association
  • Texas A&M University
  • BikeTexas Education Fund
  • City of Austin, Neighborhood Connectivity Division
  • Seton Asthma Center
  • American Lung Association of the Central States
  • Ventanilla de Salud
  • Austin Parks and Recreation
  • ACTIVE Life, Inc.
  • Michael & Susan Dell Center for Advancement of Healthy Living, UT School of Public Health
measuring air quality
Measuring Air Quality
  • Cigarettes, cigars and pipes are major emitters of respirable suspended particles less than 2.5 microns (PM2.5) in diameter that are easily inhaled deep into the lungs
  • TSI SidePak AM510 Personal Aerosol Monitor (weight: 1 lb)
  • This instrument measures and records in memory the PM2.5 concentration every minute during sampling
  • PM2.5 very sensitive marker of SHS
levels of shs outdoors close to levels indoors
Levels of SHS Outdoors Close to Levels Indoors

Levels of small particles detected at varying distances from 5 cigarettes outdoors, compared to indoor living room

Indoor level

Klepeis NE et al (2007). Real-time measurement of outdoor tobacco smoke particles. J Air Waste Mgmt Assoc 57:522-534.

questions addressed by system dynamics modeling learning to re direct the course of change

Historical

Markov Forecasting Model

Data

Simulation Experiments

in

Action Labs

Questions Addressed by System Dynamics ModelingLearning to Re-Direct the Course of Change

Prevalence of Diagnosed Diabetes, US

40

Where?

30

What?

Million people

20

How?

  • Markov Model Constants
  • Incidence rates (%/yr)
  • Death rates (%/yr)
  • Diagnosed fractions
  • (Based on year 2000 data, per demographic segment)

10

Who?

Why?

0

1980

1990

2000

2010

2020

2030

2040

2050

Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003;6:155-164.

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

diabetes burden is driven by population flows

Burden of

Diabetes

Costs

(per person with diabetes)

Total Prevalence

Unhealthy Days

(people with diabetes)

(per person with

diabetes)

Developing

PreDiabetes

Diabetes

People with

Deaths

People with

Diagnosis

People with

Onset

People with

Onset

Normal

e

Undiagnosed

Diagnosed

Undiagnosed

a

b

c

Blood Sugar

Diabetes

Diabetes

PreDiabetes

Levels

Recovering from

PreDiabetes

Deaths

Obesity in the

PreDiabetes

Diabetes

Diabetes

General

Detection &

Diagnosis

Management

Population

Management

Volume

Diabetes Burden is Driven by Population Flows

Developing

d

Inflow

Outflow

diabetes burden is driven by population flows92

Burden of

This larger view takes us beyond standard epidemiological models and most intervention programs

Diabetes

Costs

(per person with diabetes)

Total Prevalence

Unhealthy Days

(people with diabetes)

(per person with

diabetes)

Developing

PreDiabetes

Diabetes

People with

Deaths

People with

Diagnosis

People with

Onset

People with

Onset

Normal

e

Undiagnosed

Diagnosed

Undiagnosed

a

b

c

Blood Sugar

Diabetes

Diabetes

PreDiabetes

Levels

Recovering from

PreDiabetes

Deaths

Obesity in the

PreDiabetes

Diabetes

Diabetes

General

Detection &

Diagnosis

Management

Population

Management

Volume

Standard boundary

Diabetes Burden is Driven by Population Flows

Developing

d

Inflow

Outflow

diabetes system modeling project confirming fit to historical trends 2 examples out of 10

Simulated

Simulated

Diabetes System Modeling ProjectConfirming Fit to Historical Trends (2 examples out of 10)

Obese % of Adults

Diagnosed Diabetes % of Adults

40%

8%

Obese % of adults

Diagnosed diabetes % of adults

30%

6%

20%

4%

Data (NHIS)

Data (NHANES)

10%

2%

0%

0%

1980

1985

1990

1995

2000

2005

2010

1980

1985

1990

1995

2000

2005

2010

the growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence

Risk multiplier on diabetes onset from obesity = 2.6

The growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence

Obese Fraction and Diabetes per Thousand

130

0.7

Diabetes Prevalence

85

0.35

Obesity Prevalence

40

0

1980

1990

2000

2010

2020

2030

2040

2050

Time (Year)

slide95

Risk multiplier on diabetes onset from obesity = 2.6

Baseline Scenario: Obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years

Obese Fraction and Diabetes per Thousand

Onset=6.3 per thou

130

0.7

Estimated 2006 values

Diabetes Prevalence

Prevalence=92 AND RISING

85

0.35

Obesity Prevalence

Death=3.8

per thou

40

0

1980

1990

2000

2010

2020

2030

2040

2050

Time (Year)

With high (even if flat) onset, prevalence tub keeps filling until deaths (4-5%/yr)=onset

Diabetes prevalence keeps growing after obesity stops

WHY?

slide96

Reduction in unhealthy days per complicated case if conventionally managed: 33%;

if intensively managed: 67%

Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future

Unhealthy Days per Thou and Frac Managed

Obese Fraction and Diabetes per Thousand

500

Managed fraction

130

0.65

0.7

Diabetes Prevalence

375

85

0.325

0.35

Obesity Prevalence

Unhealthy Days

from Diabetes

40

250

0

0

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

Time (Year)

Diabetes prevalence keeps growing after obesity stops

If disease management gains end, the burden grows

a sequence of what if simulations
A Sequence of What-if Simulations

Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006

People with Diabetes per Thousand Adults

Monthly Unhealthy Days from Diabetes per Thou

150

500

Base

450

125

Base

400

100

350

75

300

50

250

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

further increases in diabetes management

Diabetes mgmt does nothing to slow the growth of prevalence—in fact, it increases it. As soon as diabetes mgmt stops improving, unhealthy days start to grow as fast as prevalence.

Further Increases in Diabetes Management

Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.) What do you think will happen?

People with Diabetes per Thousand Adults

Monthly Unhealthy Days from Diabetes per Thou

150

500

Base

Diab mgt

450

125

Base

400

Diab mgt

100

350

75

300

50

250

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

Keeping the burden at bay for nine years longer

More people living with diabetes

a huge push for prediabetes management

Diabetes onset rate reduced 12% relative to base run. Not nearly enough to offset the excess onset due to high obesity. By 2050, diabetes prevalence reduced only 9% relative to base run.

A Huge Push for Prediabetes Management

Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.) No increase in diabetes mgmt. What do you think will happen?

People with Diabetes per Thousand Adults

Monthly Unhealthy Days from Diabetes per Thou

150

500

Base

Base

450

125

PreD mgmt

400

PreD mgmt

100

350

75

300

50

250

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

The improvement is relatively modest—the growth is not stopped

two scenarios in which obesity is reduced
Two Scenarios in which Obesity is Reduced

What if it were possible—in addition to the prediabetes mgmt intervention - to gradually lower the fraction obese from 34% (2006) to the 1994 value of 25% by 2030? Or, to the 1984 value of 18%?

Obese Fraction of Adult Population

0.4

Base

0.3

Obesity 25%

Obesity 18%

0.2

0.1

0

1980

1990

2000

2010

2020

2030

2040

2050

managing prediabetes and reducing obesity

Why is obesity reduction so powerful?

Mainly because of its strong effect on onset rate among prediabetics; but, also, because it reduces PreD prevalence itself. However, achieving significant obesity reduction takes a long time.

Managing Prediabetes AND Reducing Obesity

What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more?

People with Diabetes per Thousand Adults

Monthly Unhealthy Days from Diabetes per Thou

150

500

Base

450

Base

PreD mgmt

125

PreD mgmt

400

PreD & Ob 25%

PreD & Ob 25%

100

350

PreD & Ob 18%

75

PreD & Ob 18%

300

50

250

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

The more you reduce obesity, the sooner you stop the growth in diabetes—and the more you bring it down

… Same with the burden of diabetes

intervening effectively upstream and downstream

Downstream improvement acts quickly against burden but cannot continue forever. Significant upstream gains are thus essential but will likely take 15+ years to achieve. A flat-burden future is possible but requires simultaneous action on both fronts.

Intervening Effectively Upstream AND Downstream

With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes mgmt intervention on top of the PreD+Ob25 one?

People with Diabetes per Thousand Adults

Monthly Unhealthy Days from Diabetes per Thou

150

500

Base

450

Base

125

PreD mgmt

PreD mgmt

400

All 3

100

Pred & Ob 25%

PreD & Ob 25%

350

All 3 --

PreD & Ob 25% & Diab mgmt

75

300

50

250

1980

1990

2000

2010

2020

2030

2040

2050

1980

1990

2000

2010

2020

2030

2040

2050

With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat through 2050!

assumptions for future scenarios
Assumptions for Future Scenarios

Base Case

  • Caloric balances stay at 2000 values through 2050

Altering Food and Activity Environments

  • Reduce caloric balances to their 1970 values by 2015
  • Focused on
    • ‘School Youth’: youth ages 6-19
    • ‘All Youth’: all youth ages 0-19
    • ‘School+Parents’: school youth plus their parents
    • ‘All Adults’: all adults ages 20+
    • ‘All Ages’: all youth and adults

Subsidized Weight Loss Programs for Obese Individuals

  • Net daily caloric reduction of program is 40 calories/day (translates to 1.8 kg weight loss per year)
  • Fully effective by 2010 and terminated by 2020
alternative futures obesity in adults 20 74
Alternative FuturesObesity in Adults (20-74)

Obese fraction of Adults (Ages 20-74)

50%

40%

30%

Fraction of popn 20-74

20%

10%

0%

1970

1980

1990

2000

2010

2020

2030

2040

2050

Base

SchoolYouth

AllYouth

School+Parents

AllAdults

AllAges

AllAges+WtLoss

slide105

PRISM Base Case Behavior 2000 – 2040

for East Travis

Junk food

interventions: Tax,

Tobacco interventions: Tax, restrict

counter-market

marketing, counter-market, ban

smoking in public places, increase

Reduce

Increase use of

Fruit & vegetable

interventions:

Provide access,

promote

use of quit services

particulate air

weight loss services

pollution

by obese

Smoker

Prevalence

0.4

0.8

0.6

Secondhand

Smoke

0.6

Excess Junk

Food Frac.

30

Air Pollution

PM2.5

Obesity

Prevalence

0

0

0

0

4

0

Fruit/Veg-Poor

Diet Fraction

0.8

Non-CV deaths

per 1000

Chronic Disorders

0

0.5

0

Uncntrld

Disorders

4,000

10,000

High

cholesterol

40

0.4

CV Events

per 1000

Total Conseq

Costs per Cap

Average sodium

Consumption

in mg

High BP

Chol

Reduce use of

Sodium

BP

sodium in food

0

consumption

0

Diab

0

0

Diabetes

0

20

CV Deaths

per 1000

0.8

Use of Quality

Preventive Care

non-CVD

1

0.04

Inadequate

PA Fraction

Trans Fat Frac.

of Calories

0.2

0

Distressed

fraction

0

0

0

Fraction of

popn 65+

0.4

0.1

Improve quality of

acute and rehab

care for CV events

Physical activity interventions:

Increase provision

and use of quality

Preventive care

0

Provide access, promote,

Reduce use of

increase school & childcare

trans fats in food

Increase use of help

requirements

services for distress

what results should we expect to see in austin travis county with arra related interventions
What results should we expect to see in Austin/Travis County with ARRA-related interventions?

Tax tobacco = 0.0

Restrict tobacco sales & marketing = 0.5

Tobacco counter-marketing = 1.0

Ban workplace smoking = 0.6

Use of smoking quit services = 1.0

Air pollution (PM2.5) = 0.0

Tax junk food = 0.0

Junk food counter-marketing = 1.0

Access to fruit & vegetables = 1.0

Promote fruit &veg consumption =1.0

Sodium consumption = 0.0

Trans fat consumption = 0.0

Use of weight loss services = 0.0

  • Access to physical activity spaces = 1.0
  • Promote physical activity = 0.5
  • Require more physical activity in school = 1.0
  • Physical activity in childcare = 0.5
  • Use of quality preventive care, no CV event = 0.5
  • Use of quality of preventive care, post CV event = 0.5
  • Use of quality of acute and rehab care for CV events = 0.0
  • Use of help services for distressed, no CV event = 0.0
  • Use of help services for distressed, post CV event = 0.0
slide107
Austin Intervention Scenarios: Expected ARRA EffectsTobacco + Individual Services + Diet & PA + Air Pollution & Sodium & Trans fat

Smoking Prevalence (Adults)

Obesity Prevalence (Adults)

0.6

0.4

0.3

0.3

0.2

0.1

0

0

2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Cardiovascular Events per 1000(CHD, Stroke, CHF, PAD)

Deaths from All Risk Factors per 1,000

40

8

30

6

20

4

10

2

0

0

2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

**if all risk factors=0**

slide108
Austin Intervention Scenarios: Expected ARRA EffectsTobacco + Individual Services + Diet & PA + Air Pollution & Sodium & Trans fat

Years of Life Lost from Attributable Deaths

Consequence Costs per Capita

(medical costs + productivity)

80,000

6,000

4,500

60,000

40,000

3,000

people*years/Year

20,000

1,500

0

0

2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Time (Year)

Time (Year)

**if all risk factors=0**

conclusions
Conclusions
  • Planned tobacco, diet and PA interventions in Austin/Travis County will reduce
    • total consequence costs by $463 per capita per year,

or $ 12.9 million each year

    • the estimated number of deaths in Travis county by 570 people by 2015, and 2000 by 2040.
  • Adding Sodium and Salt reduction to the plan could save an additional
    • $225 per capita per year of total consequence costs,

or $ 6.3 million each year

    • 250 lives in Travis county by 2015, and 3000 more by 2040.