Presentation is loading. Please wait.

Presentation is loading. Please wait.

Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas Public Health Association Galveston, TX February 26, 2007.

Similar presentations


Presentation on theme: "Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas Public Health Association Galveston, TX February 26, 2007."— Presentation transcript:

1 Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas Public Health Association Galveston, TX February 26, 2007 Syndemics Prevention Network Bobby Milstein Syndemics Prevention Network Centers for Disease Control and Prevention Atlanta, Georgia bmilstein@cdc.gov http://www.cdc.gov/syndemics

2 Syndemics Prevention Network Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. Journal of the American Medical Association 2001;286(10):1195-200. Kaufman FR. Diabesity: the obesity-diabetes epidemic that threatens America--and what we must do to stop it. New York, NY: Bantam Books, 2005.

3 Syndemics Prevention Network Imperatives for Protecting Health Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11):1403-1406. Typical Current State Static view of problems that are studied in isolation Proposed Future State Dynamic systems and syndemic approaches "Currently, application of complex systems theories or syndemic science to health protection challenges is in its infancy.“ -- Julie Gerberding

4 Syndemics Prevention Network Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521. How Many Triangles Do You See?

5 Syndemics Prevention Network Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; 2002. p. 41-42.. Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf Boundary Critique When it comes to the problem of boundary judgments, experts have no natural advantage of competence over lay people. -- Werner Ulrich

6 Syndemics Prevention Network Transforming the Future of Diabetes… "Every new insight into Type 2 diabetes... makes clear that it can be avoided--and that the earlier you intervene the better. The real question is whether we as a society are up to the challenge... Comprehensive prevention programs aren't cheap, but the cost of doing nothing is far greater..." Gorman C. Why so many of us are getting diabetes: never have doctors known so much about how to prevent or control this disease, yet the epidemic keeps on raging. how you can protect yourself. Time 2003 December 8. Accessed at http://www.time.com/time/covers/1101031208/story.html. …in an Era of Rising Obesity

7 Syndemics Prevention Network Prevalence of Diagnosed Diabetes, US 0 10 20 30 40 19801990200020102020203020402050 Million people Historical Data Markov Model Constants Incidence rates (%/yr) Death rates (%/yr) Diagnosed fractions (Based on year 2000 data, per demographic segment) 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. Why? Where? How? Who? What? Markov Forecasting Model Simulation Experiments in Action Labs Questions Addressed by System Dynamics Modeling Learning to Re-Direct the Course of Change

8 Syndemics Prevention Network Time Series Models Describe trends Multivariate Stat Models Identify historical trend drivers and correlates Patterns Structure Events Increasing: Depth of causal theory Data and sensitivity testing requirements Robustness for longer- term projection Value for developing policy insights Increasing: Depth of causal theory Data and sensitivity testing requirements Robustness for longer- term projection Value for developing policy insights Dynamic Simulation Models Anticipate new trends, learn about policy consequences, and set justifiable goals Tools for Policy Analysis

9 Syndemics Prevention Network A Model Is… An inexact representation of the real thing It helps us understand, explain, anticipate, and make decisions “All models are wrong, some are useful.” -- George Box “All models are wrong, some are useful.” -- George Box

10 Syndemics Prevention Network System Dynamics Simulation Modeling Was Developed to Address Problems Marked by Dynamic Complexity Good at Capturing Differences between short- and long-term consequences of an action Time delays (e.g., transitions, detection, response) Accumulations (e.g., prevalence, capacity) Behavioral feedback (e.g., actions trigger reactions) Nonlinear causal relationships (e.g., effect of X on Y is not constant-sloped) Differences or inconsistencies in goals/values among stakeholders Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000. Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458 Origins Jay Forrester, MIT (from late 1950s) Public policy applications starting late 1960s

11 Syndemics Prevention Network Understanding Dynamic Complexity From a Very Particular Distance “{System dynamics studies problems} from ‘a very particular distance', not so close as to be concerned with the action of a single individual, but not so far away as to be ignorant of the internal pressures in the system.” -- George Richardson Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3):53-68. Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, 1999. Available at. Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

12 Syndemics Prevention Network Simulations for Learning in Dynamic Systems Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000. Homer JB. Why we iterate: Scientific modeling in theory and practice. System Dynamics Review 1996; 12(1):1-19. Multi-stakeholder Dialogue Dynamic Hypothesis (Causal Structure)Plausible Futures (Policy Experiments)

13 Syndemics Prevention Network CDC Diabetes System Modeling Project Discovering Dynamics Through Action Labs 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.

14 Syndemics Prevention Network CDC Dara Murphy, Bobby Milstein, Chris Benjamin, Wayne Millington, Parul Nanavati, Sharon Daves, Frank Vinicor, Mark Rivera Contractor Team Drew Jones Jack Homer Joyce Essien Doc Klein Don Seville State Diabetes Programs Minnesota Heather Devlin, Jay Desai California Gary He, Karen Black, Toshi Hayashi Vermont Robin Edelman, Jason Roberts, Ellen Thompson CDC Diabetes System Modeling Project Contributors

15 Syndemics Prevention Network Project Background Diabetes programs face tough challenges and questions –Pressure for results on disease burden, not just behavioral change –The Diabetes Prevention Program indicates primary prevention is possible, but may be difficult and costly –What is achievable on a population level? –How should funds be allocated? Standard epidemiological models rarely address such policy questions In Fall 2003, CDC initiates System Dynamics modeling project In Spring 2005, some states join as collaborators in further developing and using the SD model

16 Syndemics Prevention Network Inflow Volume Outflow Developing Burden of Diabetes Total Prevalence (people with diabetes) Unhealthy Days (per person with diabetes) Costs (per person with diabetes) People with Diagnosed Diabetes Diagnosis Deaths a b People with Undiagnosed PreDiabetes Developing Diabetes Onset c d People with Normal Blood Sugar Levels PreDiabetes Onset Recovering from PreDiabetes e Diabetes Management Diabetes Diagnosis Obesity in the General Population PreDiabetes Detection & Management People with Undiagnosed Diabetes Deaths Diabetes Burden is Driven by Population Flows

17 Syndemics Prevention Network Diabetes Burden is Driven by Population Flows Inflow Volume Outflow Developing Burden of Diabetes Total Prevalence (people with diabetes) Unhealthy Days (per person with diabetes) Costs (per person with diabetes) People with Diagnosed Diabetes Diagnosis Deaths a b People with Undiagnosed PreDiabetes Developing Diabetes Onset c d People with Normal Blood Sugar Levels PreDiabetes Onset Recovering from PreDiabetes e Diabetes Management Diabetes Diagnosis Obesity in the General Population PreDiabetes Detection & Management People with Undiagnosed Diabetes Deaths Standard boundary This larger view takes us beyond standard epidemiological models and most intervention programs

18 Syndemics Prevention Network Using Available Data to Calibrate the Model Information SourcesData U.S. Census Population growth and death rates Fractions elderly, black, hispanic Health insurance coverage National Health Interview Survey Diabetes prevalence Diabetes detection National Health and Nutrition Examination Survey Prediabetes prevalence Obesity prevalence Behavioral Risk Factor Surveillance System Eye exam and foot exam Taking diabetes medications Unhealthy days (HRQOL) Professional Literature Effects of risk factors and mgmt on onset, complications, and costs Direct and indirect costs of diabetes

19 Syndemics Prevention Network Diabetes System Modeling Project Confirming Fit to Historical Trends (2 examples out of 10) Diagnosed Diabetes % of AdultsObese % of Adults 0% 10% 20% 30% 40% 1980198519901995200020052010 Obese % of adults Data (NHANES) Simulated 0% 2% 4% 6% 8% 1980198519901995200020052010 Diagnosed diabetes % of adults Data (NHIS) Simulated

20 Syndemics Prevention Network The growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence Obese Fraction and Diabetes per Thousand 130 0.7 85 0.35 40 0 19801990200020102020203020402050 Time (Year) Diabetes Prevalence Obesity Prevalence Risk multiplier on diabetes onset from obesity = 2.6

21 Syndemics Prevention Network Prevalence=92 AND RISING Although we expect obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years Obese Fraction and Diabetes per Thousand 130 0.7 85 0.35 40 0 19801990200020102020203020402050 Time (Year) Diabetes Prevalence Obesity Prevalence Diabetes prevalence keeps growing after obesity stops WHY? With high (even if flat) onset, prevalence tub keeps filling until deaths (4-5%/yr)=onset Onset=6.3 per thou Estimated 2006 values Death=3.8 per thou Prevalence= 92 / thou Risk multiplier on diabetes onset from obesity = 2.6

22 Syndemics Prevention Network 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 130 0.7 85 0.35 40 0 19801990200020102020203020402050 Time (Year) Diabetes Prevalence Obesity Prevalence 500 0.65 250 0 19801990200020102020203020402050 375 0.325 Unhealthy Days from Diabetes Managed fraction Diabetes prevalence keeps growing after obesity stops If disease management gains end, the burden grows Reduction in unhealthy days per complicated case if conventionally managed: 33%; if intensively managed: 67%

23 Syndemics Prevention Network A Sequence of What-if Simulations People with Diabetes per Thousand Adults 150 125 100 75 50 19801990200020102020203020402050 Monthly Unhealthy Days from Diabetes per Thou 500 450 400 350 300 250 19801990200020102020203020402050 Base Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006

24 Syndemics Prevention Network Further Increases in Diabetes Management People with Diabetes per Thousand Adults 150 125 100 75 50 19801990200020102020203020402050 Monthly Unhealthy Days from Diabetes per Thou 500 450 400 350 300 250 19801990200020102020203020402050 Base Diab mgt Base More people living with diabetes Keeping the burden at bay for nine years longer Diab mgt 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? 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.

25 Syndemics Prevention Network A Huge Push for Prediabetes Management People with Diabetes per Thousand Adults 150 125 100 75 50 19801990200020102020203020402050 Monthly Unhealthy Days from Diabetes per Thou 500 450 400 350 300 250 19801990200020102020203020402050 Base PreD mgmt Base PreD mgmt The improvement is relatively modest—the growth is not stopped 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? 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.

26 Syndemics Prevention Network Two Scenarios in which Obesity is Reduced Obese Fraction of Adult Population 0.4 0.3 0.2 0.1 0 19801990200020102020203020402050 Base Obesity 25% Obesity 18% 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%?

27 Syndemics Prevention Network Managing Prediabetes AND Reducing Obesity 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 People with Diabetes per Thousand Adults 150 125 100 75 50 19801990200020102020203020402050 Monthly Unhealthy Days from Diabetes per Thou 500 450 400 350 300 250 19801990200020102020203020402050 Base PreD mgmt PreD & Ob 25% PreD & Ob 18% Base PreD mgmt PreD & Ob 18% PreD & Ob 25% 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? 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.

28 Syndemics Prevention Network Intervening Effectively Upstream AND Downstream People with Diabetes per Thousand Adults 150 125 100 75 50 19801990200020102020203020402050 Monthly Unhealthy Days from Diabetes per Thou 500 450 400 350 300 250 19801990200020102020203020402050 Base PreD mgmt Base PreD & Ob 25% Pred & Ob 25% All 3 -- PreD & Ob 25% & Diab mgmt All 3 With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat through 2050! 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? 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.

29 Syndemics Prevention Network Cover of "The Economist", Dec. 13-19, 2003 Cover of "The Economist", Dec. 13-19, 2003.

30 Syndemics Prevention Network CDC Obesity Dynamics Modeling Project Contributors Core Design Team Dave Buchner Andy Dannenberg Bill Dietz Deb Galuska Larry Grummer-Strawn Anne Hadidx Robin Hamre Laura Kettel-Khan Elizabeth Majestic Jude McDivitt Cynthia Ogden Michael Schooley System Dynamics Consultants Jack Homer Gary Hirsch Time Series Analysts Danika Parchment Cynthia Ogden Margaret Carroll Hatice Zahran Project Coordinator Bobby Milstein Workshop Participants Atlanta, GA: May 17-18 (N=47) Lansing, MI: July 26-27 (N=55) Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.

31 Syndemics Prevention Network The Rise and Future Fall of Obesity The Why and the How in Broad Strokes Fraction of Obese Individuals & Prevalence of Related Health Problems Time Overweight & Obesity Prevalence R Engines of Growth Health Protection Efforts - B Responses to Growth Resources & Resistance - B Obstacles Broader Benefits & Supporters R Reinforcers Drivers of Unhealthy Habits Engines of Growth R1 Spiral of poor health and habits R2 Parents and peer transmission R3 Media mirrors R4 Options shape habits shape options R5 Society shapes options shape society Engines of Growth R1 Spiral of poor health and habits R2 Parents and peer transmission R3 Media mirrors R4 Options shape habits shape options R5 Society shapes options shape society Responses to Growth B1 Self-improvement B2 Medical response B3 Improving preventive healthcare B4 Creating better messages B5 Creating better options in beh. settings B6 Creating better conditions in wider environ B7 Addressing related health conditions Responses to Growth B1 Self-improvement B2 Medical response B3 Improving preventive healthcare B4 Creating better messages B5 Creating better options in beh. settings B6 Creating better conditions in wider environ B7 Addressing related health conditions Resources, Resistance, Benefits & Supports R6 Disease care costs squeeze prevention B8 Up-front costs undercut protection efforts B9 Defending the status quo B10 Potential savings build support R7 Broader benefits build support Resources, Resistance, Benefits & Supports R6 Disease care costs squeeze prevention B8 Up-front costs undercut protection efforts B9 Defending the status quo B10 Potential savings build support R7 Broader benefits build support

32 Syndemics Prevention Network Focus of Our Simulation Model Explore effects of new interventions affecting caloric balance (intake less expenditure) –U.S. policy discourse is primarily focused on: prevention among school-aged youth medical treatment for the severely obese –What are the likely consequences? How much impact on adult obesity? How long will it take to see? Should we target other subpopulations? (age, sex, weight category) Consider two classes of interventions –Changes in food & activity environments –Weight loss/maintenance services for individuals Additional intervention details (composition, coverage, efficacy, cost) left outside model boundary for now –Available data are inadequate to quantify impacts and cost-effectiveness –Could stakeholder Delphi help?

33 Syndemics Prevention Network Obesity Dynamics Over the Decades Dynamic Population Weight Framework

34 Syndemics Prevention Network Obesity Dynamics Over the Decades Dynamic Population Weight Framework Dynamic Population Weight Framework Population by Age (0-99) and Sex Flow-rates between BMI categories Overweight and obesity prevalence Birth Immigration Death Yearly aging Not Overweight Moderately Overweight Moderately Obese Severely Obese

35 Syndemics Prevention Network Obesity Prevalence Over the Decades Dynamic Population Weight Framework Not Overweight Moderately Overweight Moderately Obese Severely Obese Not Overweight Moderately Overweight Moderately Obese Severely Obese Not Overweight Moderately Overweight Moderately Obese Severely Obese Births Age 0 Age 1 Age 99 No Change in BMI Category (maintenance flow) Increase in BMI Category (up-flow) Decline in BMI Category (down-flow)

36 Syndemics Prevention Network Obesity Dynamics Over the Decades Dynamic Population Weight Framework Dynamic Population Weight Framework Population by Age (0-99) and Sex Flow-rates between BMI categories Overweight and obesity prevalence Birth Immigration Death Caloric Balance Yearly aging Not Overweight Moderately Overweight Moderately Obese Severely Obese

37 Syndemics Prevention Network Obesity Dynamics Over the Decades Two Classes of Interventions Dynamic Population Weight Framework Population by Age (0-99) and Sex Flow-rates between BMI categories Overweight and obesity prevalence Birth Immigration Death Caloric Balance Yearly aging Not Overweight Moderately Overweight Moderately Obese Severely Obese Trends and Planned Interventions Changes in the Physical and Social Environment Weight Loss/Maintenance Services for Individuals

38 Syndemics Prevention Network Obesity Dynamics Over the Decades Many Environmental Factors Come Into Play Dynamic Population Weight Framework Population by Age (0-99) and Sex Flow-rates between BMI categories Overweight and obesity prevalence Birth Immigration Death Caloric Balance Yearly aging Not Overweight Moderately Overweight Moderately Obese Severely Obese Trends and Planned Interventions Changes in the Physical and Social Environment Weight Loss/Maintenance Services for Individuals Food Price Smoking Social Influences on Consumption & Selection Options for Affordable Recommended Foods (Work, School, Markets, Restaurants) Activity Limiting Conditions Options for Safe, Accessible Physical Activity (Work, School, Neighborhoods) Distance from Home to Work, School, Errands Electronic Media in the Home Social Influences on Active/Inactive Options Activity Environment Food Environment

39 Syndemics Prevention Network Information Sources Topic AreaData Source Prevalence of Overweight and Obesity BMI prevalence by sex and age (10 age ranges) National Health and Nutrition Examination Survey (1971-2002) Translating Caloric Balances into BMI Flow-Rates BMI category cut-points for children and adolescentsCDC Growth Charts Median BMI within each BMI category National Health and Nutrition Examination Survey (1971-2002) Median height Average kilocalories per kilogram of weight changeForbes 1986 Estimating BMI Category Down-Flow Rates In adults: Self-reported 1-year weight change by sex and age NHANES (2001-2002) *indicates 7-12% per year* In children: BMI category changes by grade and starting BMI Arkansas pre-K through 12 th grade assessment (2004-2005) *indicates 15-28% per year* Population Composition Population by sex and age U.S. Census and Vital Statistics (1970-2000 and projected) Death rates by sex and age Birth and immigration rates Influence of BMI on Mortality Impact of BMI category on death rates by ageFlegal, Graubard, et al. 2005.

40 Syndemics Prevention Network Calibration of Uncertain Parameters To Reproduce 60 BMI Prevalence Time Series (10 age ranges x 2 sexes x 3 high-BMI categories) Step 1: Iteratively adjust up-rate and down-rate constants and initial BMI prevalences to reproduce steady-state BMI prevalence for the early 1970s Step 2: Adjust 57 caloric balance time series (by age, sex, and BMI category, 1975-2000) to reproduce BMI prevalence growth for the 1980s and 1990s

41 Syndemics Prevention Network (a) Overweight fraction 0% 20% 40% 60% 80% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated (b) Obese fraction 0% 10% 20% 30% 40% 50% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated (c) Severely obese fraction 0% 5% 10% 15% 20% 25% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated Reproducing Historical Trends One of 20 {sex, age} Subgroups: Females age 55-64 Note: S-shaped curves, with inflection in the 1990s

42 Syndemics Prevention Network Explaining BMI Prevalence Growth: Age-to-Age Carryover + Caloric Imbalance Example: Females Age 55-64 Overweight fractions of middle-aged women 0% 20% 40% 60% 80% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Obese fractions of middle-aged women 0% 10% 20% 30% 40% 50% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Severely obese fractions of middle-aged women 0% 5% 10% 15% 20% 25% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Estimated caloric imbalances for women age 55-64 0 5 10 15 20 19701975198019851990199520002005 Kcal per day Not overwtMod overwtObese

43 Syndemics Prevention Network 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

44 Syndemics Prevention Network Alternative Futures Obesity in Adults (20-74) Obese fraction of Adults (Ages 20-74) 0% 10% 20% 30% 40% 50% 197019801990200020102020203020402050 Fraction of popn 20-74 BaseSchoolYouthAllYouth School+ParentsAllAdultsAllAges AllAges+WtLoss

45 Syndemics Prevention Network Findings Inflection point in obesity probably occurred during the 1990s –Simple extrapolations based on 1990s growth likely exaggerate future prevalences Caloric imbalance vs. 1970 only 1-2% (less than 50 cal./day) within any given age, sex, and BMI category –Most of observed 9-13% cal./day increase in intake (USDA 1977-1996) has been natural consequence of weight gain (via metabolic adjustment), not its cause Impacts of changing environments on adult obesity take decades to play out fully: “Carryover effect” Youth interventions have only small impact on overall adult obesity –Assumes (1) adult habits determined by adult environment, and (2) childhood overweight causes no irreversible metabolic changes Weight-loss for the obese could accelerate progress--but, first, an effective program that minimizes recidivism must be found

46 Syndemics Prevention Network Conclusions & Limitations This model improves our understanding of population dynamics of weight change and supports pragmatic planning/evaluation –No other analytical model plays out effects of changes in caloric balance on BMI prevalences over the life-course –Traces plausible impacts of population-level and individual-level interventions And addresses questions of whom to target, by how much, and by when But it has limitations—some addressable, some due to lack of data –Does not indicate exact nature of interventions Does not address cost-effectiveness of interventions, nor political reinforcement and resistance –Does not address racial/ethnic sub-groups –Does not trace individual life histories (compartmental structure) –Assumes habits determined by current environment, not by childhood learning –Assumes no irreversible metabolic changes sustained as a result of childhood overweight/obesity

47 Syndemics Prevention Network Learning In and About Dynamic Systems Benefits of Simulation/Game-based Learning Formal means of evaluating options Experimental control of conditions Compressed time Complete, undistorted results Actions can be stopped or reversed Visceral engagement and learning Tests for extreme conditions Early warning of unintended effects Opportunity to assemble stronger support Dynamic Complexity Hinders… Generation of evidence (by eroding the conditions for experimentation) Learning from evidence (by demanding new heuristics for interpretation) Acting upon evidence (by including the behaviors of other powerful actors) Sterman JD. Learning from evidence in a complex world. American Journal of Public Health (in press). Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000. “In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies." -- John Sterman

48 Syndemics Prevention Network “Simulation is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid to intuition.” -- Robert Axelrod Axelrod R. Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P, editors. Simulating Social Phenomena. New York, NY: Springer; 1997. p. 21-40.. Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000. Simulation Experiments Open a Third Branch of Science “The complexity of our mental models vastly exceeds our ability to understand their implications without simulation." -- John Sterman How? Where? Prevalence of Obese Adults, United States Why? Data Source: NHANES 2020 2010 Who? What?

49 Syndemics Prevention Network For Additional Information http://www.cdc.gov/syndemics


Download ppt "Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas Public Health Association Galveston, TX February 26, 2007."

Similar presentations


Ads by Google