MediSaveTM: New Software toCalculate the Return on
Investment of
Tobacco Control Programs
Steven S. Foldes, Ph.D. andAnju Joglekar, Ph.D.
Blue Cross and Blue Shield of Minnesotaand Medical Scientists, Inc.
November 19, 2002
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Making the Business Case Why calculate ROI?
Tobacco is the leading preventable cause of death and disease, and that tobacco costs the nation $50 billion in direct medical costs annually.
Regrettably, this is not always sufficiently persuasive. Need is to demonstrate ROI for the interventions you plan to field in your specific population.
“The single most important piece of information that employers want to see about tobacco control is the return on investment.” Christopher Queram, CEO of Employer Health Care Alliance Cooperative of Wisconsin , at the Addressing Tobacco in Managed Care Conference, April 10, 2002
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Making the Business Case Value of modeling smoking interventions
Retrospectively answer evaluation questions Prospectively examine multiple scenarios
Important with prevention efforts that have desired outcomes mostly in the future but immediate costs
Helps reinforce importance of interventions and persuade others to adopt them
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Other Tools SAMMEC
Models smoking-attributable mortality Strong scientific base Credibility of the CDC Does not model future events or costs Not designed to calculate ROI due to interventions
SimSmoke Models future impact of multiple interventions, including policy-related interventions Strong scientific base Focus is on smoking rates and smoking-attributable mortality, not on costs
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Why MediSaveTM? MediSaveTM: premier ROI calculation software for disease management programs
Medical Scientists, Inc. has demonstrated ability to create effective, user-friendly software $1 million platform
Strong scientific base Useful in a health plan context Highly flexible
Redesigned for tobacco Single disease v. root cause for multiple diseases Specific interventions
Incorporates SimSmoke outputs for policy-related interventions
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MediSaveTM is a predictive modeling software suite that forecasts the clinical and financial impact of a pharmaceutical or medical technology.
What is MediSave™?What is MediSave™?
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User Defined
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Joint Project Team Medical Scientists, Inc.
Anju Joglekar, Ph.D. Paula Kadison, M.D. Rebecca Chao, M.P.H.
Blue Cross and Blue Shield of Minnesota Steven S. Foldes, Ph.D. Marc Manley, M.D., M.P.H. Nina Alesci, M.P.H. Xiaohong Chen, M.S. Monica Schultz, M.S.
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MediSaveTM Model The model estimates all DIRECT health care outcomes and costs for a given population by condition and smoking
status. Based on:
Relative risks from epidemiological literature Administrative claims data
20 year projected incidence of 39 conditions: 17 smoking related 22 unrelated to smoking
Estimated costs: Professional, facility, lab/radiology/pathology, pharmacy
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MediSaveTM Model
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MediSaveTM Model Model also estimates INDIRECT morbidity and mortality costs by smoking status.
Based on: Worksite studies Government sources, e.g. NHIS
Outcomes and costs of interest: Lost work days due to illness (by smoking status) Lost work time due to smoking breaks Years of potential life lost (YPLL) Employee replacement costs due to smoking attributable deaths
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MediSaveTM Model
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Intervention Programs
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Perspective MediSave models morbidity and mortality costs from several perspectives:
Societal Employer Health plan
Generates projections for both medical and non-medical costs using each perspective.
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The Business Case:Baseline Projections
The user can answer questions about the “status quo,” such as: What is the expected number of cases by condition and smoking status over the next twenty years? What are the smoking attributable medical costs by condition and smoking status? What are the all medical costs by smoking status? What are lost work days by smoking status? What are non-medical costs by smoking status?
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The Business Case:Baseline Projections
*This projection is not based on real data.
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The Business Case:Baseline Projections
*This projection is not based on real data.
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The Business Case:Effect of Interventions
What is the expected ROI associated with a cessation program? How far into the future is the program break-even point? How many smokers quit due to the implementation of the program? How many new cases were averted by condition? How many hospitalizations were saved? How many work days were saved? How many lives were saved? Years of potential life saved?
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The Business Case:Effect of Interventions
*This projection is not based on real data.
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The Business Case:Effect of Interventions
*This projection is not based on real data.
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The Business Case:Effect of Interventions
*This projection is not based on real data.
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The Business Case:“What if” Scenarios
How will the ROI be affected if the: Participation in the program increases by 10%? Quit rate turns out to be 5% lower? Relapse rates are higher than the assumed rates? Program costs end up being 20% higher? Inflationary trends double?
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Major Inputs:Smoking Patterns
Prevalence of smokers, former smokers and never smokers Smoking Rates
Background quit rates Background initiation rates Relapse rate by quit duration
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Major Inputs:Smoking Patterns
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Prevalence andCosts of Conditions
Prevalence by condition Smoking related conditions Conditions unrelated to smoking Prevalence of co-morbid conditions
Medical costs by condition Claims distribution table for each condition Costs for co-morbid conditions
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Prevalence of Conditions
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Non-Medical Factors Workdays lost due to illness Average length of smoking breaks Percent employed Percent by job category Salaries by job category
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Health Plan Benefits Plan type
Gatekeeper/Open Access In network benefits
Deductible Coinsurance % Coinsurance limit
Pharmacy benefit Drug card co-pay
Plan maximum, stop-loss limit, transplant limit
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Health Plan Benefits
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Intervention Parameters Intervention Characteristics
Baseline % managed Participation rate Program costs
Prevention and Cessation Benefit Intervention quit rate Reduction in initiation rate
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Packaging of Interventions
MediSave provides the flexibility to the user to examine the impact of interventions in two ways: One intervention at a time Multiple interventions packaged together in a program
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Data Sources Epidemiologic data Administrative claims data Survey data Expert opinion
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Model Limitations Model requires many assumptions, which are only as strong as the current research base allows.
Accuracy of relative risks Particularly, decline over time for former smokers for specific conditions
Relapse rate assumptions One relapse rate regardless of prior attempts or nature of intervention
Comprehensiveness of non-medical costs e.g. Exclusion of worker’s compensation
Data limitations Not fully representative, e.g. excludes uninsured; Size of data set in relation to number of cells leaves some cells empty or small
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Model Strengths Builds on but goes beyond SAMMEC and SimSmoke
All medical costs plus non-medical costs (societal and employer perspectives) Strong evidence base
Useful in health plan populations Accounts for various benefit sets
Examines secondary and primary interventions (with input from SimSmoke) both individually and in combination
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Model Strengths Highly flexible
Allows detailed tailoring where data are available Uses best estimate “plug” numbers when specific data are not available Ability to use prospectively to model “what if” scenarios Ability to use retrospectively as part of an evaluation of implemented programs
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Conclusion Makes the business case for smoking reduction programs to
health plans employers policy makers
By calculating ROI for specific interventions in a specific population
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