medisave tm : new software to calculate the return on investment of tobacco control programs

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

2 Center for Tobacco Reduction & Health Improvement

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


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


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


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


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™?


User Defined


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.


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


MediSaveTM Model


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


MediSaveTM Model


Intervention Programs


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.


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?



*This projection is not based on real data.


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?


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?


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


Major Inputs:Smoking Patterns


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


Prevalence of Conditions


Non-Medical Factors Workdays lost due to illness Average length of smoking breaks Percent employed Percent by job category Salaries by job category


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


Health Plan Benefits


Intervention Parameters Intervention Characteristics

Baseline % managed Participation rate Program costs

Prevention and Cessation Benefit Intervention quit rate Reduction in initiation rate


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


Data Sources Epidemiologic data Administrative claims data Survey data Expert opinion


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


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


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


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

medisave tm : new software to calculate the return on investment of tobacco control programs

Documents