Using Data to Inform Policy: Principles and Some Examples from Health Care Scott Leitz Director, Office of Health Policy and Research Minnesota Department of Health August 20, 2006

Overview of Presentation  The Importance of Data to the Policy Process  Some general observations and thoughts on using data to inform policy  Four (not mutually exclusive) areas of influence: –Framing the Issue –Informing the Policymaker (and the public) and the debate –Making the Case –Developing the Solution  Summary

The Importance of Data to the Policy Process  An old saw: “The plural of anecdote is not data”  Legislators and policymakers are there to: legislate and make policy –Do so in the presence or absence of data to inform their decisions –Will use data to inform their decision but in absence of data, still need to make decision –Data and information availability doesn’t always guarantee they’ll be used to inform the decision…but lack of data guarantees that they won’t

1. Know Your Audience 1. Know your audience  A legislative audience is different than an academic audience is different from the general public  What story do you want to tell?  Educational?  Promotional?  How best is that story told?

2. Communicate to that Audience No single or clear rule of thumb on how best to communicate data. The mode depends on the audience:  One pagers  Chart packs  Talking points But in all cases, a limit number of takeaway messages (and clarity as to what those messages are) is important

3.National data is great; State data is better; local data is the best  The Tip O’Neil doctrine: “all politics is local”  Legislators and policymakers:  Will use information when it’s available  Also have a belief that their city/county/state is different (or at least want to see whether they are)  Any data helps, but the more localized the information is, the more relevant it becomes to the policymaker  Example: uninsured low income kids  Nationwide: 7.1%  VT: 1.4%; CT, MA: 2.4%  TX: 14.4%; MT: 10.6%

4. Be a Healthy Skeptic about the Source of Data  The explosion of internet sites, think tanks, and advocacy organizations has led increased availability of information on nearly any topic  On one hand: great, more information. On the other: not as great, as it can make sorting a more difficult task.  Because some organizations can bring idiological perspectives: use a healthy skepticism in choosing the studies/data sources to highlight  Refereed journals are safest; fact-based descriptive statistical information from non-partisan think tanks are also generally “safe”

5.Get to know your local university and state agency resources (and make sure they get to know you)  Academic researchers are often looking for a policy outlet for their research  State analysts oftentimes know and understand the different sources of data available  Opportunities to leverage the interest of academics in seeing their work used to bring data to bear on the topic of interest to you  Example: Rhode Island Medicaid program  Excellent in-house analysis but also a great interactive partnership with Brown University

6.And get to know your state agencies, legislative staff, advocates, and the press/media  Staff at state agencies and legislative staffers can be valuable resources in reaching policymakers  Their understanding of your story is important; they can sometimes help you to refine your message  The media can help you tell a story, especially if they can have data *and* a compelling personal story

7.Money Talks (or at least makes people interested in listening)  Programmatic outcomes are important  “did we improve the health outcomes of our population”  But oftentimes not sufficient to “sell” the program  Outcomes tied to cost savings or cost- effectiveness are important additional component  See: Rhode Island RIteCare, Minnesota uncompensated care analysis

8.Be mindful of the language that’s used in presenting data findings (and be honest with it)  How things are phrased matter  And affects credibility and believability

Between 1999 and 2004, Florida experienced a dramatic increase in the number of uninsured individuals under age 65 Source: 1999 and 2004 Florida Health Insurance Studies, referenced by Allison Hall at the State health Research and Policy Interest Group meeting, Feburary 7, 2006.

Between 1999 and 2004, Florida experienced a slight increase in the number of uninsured individuals under age 65 Source: 1999 and 2004 Florida Health Insurance Studies, referenced by Allison Hall at the State health Research and Policy Interest Group meeting, Feburary 7, 2006.

Framing The Issue

Issue Framing  Why does the issue matter?  What does the data tell us about the issue? 2 Examples:  Obesity  Insurance coverage trends –Especially related to low income kids and adults

Obesity  Obesity has been shown to be associated with increased risk of many chronic diseases, including: –Type 2 diabetes, cardiovascular disease, several types of cancer, musculoskeletal disorders, sleep apnea and respiratory problems, stroke, and gallbladder disease  Between 1987 and 2001, obesity prevalence increased 10.3 percentage points, while normal weight prevalence declined 13 percentage points (Thorpe, Health Affairs, 2004).

Obesity Trends* Among U.S. Adults BRFSS, 1985 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1986 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1987 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1988 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1989 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1990 No Data <10% 10%–14% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1991 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1992 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1993 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1994 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1995 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1996 No Data <10% 10%–14% 15%–19% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1997 No Data <10% 10%–14% 15%–19% ≥20 (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1998 No Data <10% 10%–14% 15%–19% ≥20 (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 1999 No Data <10% 10%–14% 15%–19% ≥20 (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 2000 No Data <10% 10%–14% 15%–19% ≥20 (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Obesity Trends* Among U.S. Adults BRFSS, 2001 No Data <10% 10%–14% 15%–19% 20%–24% ≥25% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

(*BMI  30, or ~ 30 lbs overweight for 5’4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person) Obesity Trends* Among U.S. Adults BRFSS, 2002

Obesity Trends* Among U.S. Adults BRFSS, 2003 (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

Obesity Trends* Among U.S. Adults BRFSS, 2004 No Data <10% 10%–14% 15%–19% 20%–24% ≥25% (*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)

Impact of Obesity on Rising Medical Spending  Obesity-related health spending accounted for 27% of inflation adjusted per capita health spending increases –41% of heart disease spending –38% of diabetes-related spending –Thorpe, October 2004, Health Affairs  Medicare will spend 34% more on an obese person than on someone of normal weight –Lakdawalla, et al., Health Affairs, September 2005

Insurance Coverage Changes  Proportion of people lacking health insurance increased to 15.7% in 2004, up from 14.0% in  45.8 million people in the U.S. lack health insurance.

Change in Number of Uninsured, 2000 to 2004 Source:John Holahan and Allison Cook, “Changes in Economic Conditions and Health Insurance Coverage, ,” Health Affairs web exclusive, November 1, All nonelderly AdultsChildren 6.0 million 6.3 million -0.3 million

Change in Number of Uninsured Adults, by Income 2000 to 2004 Source:John Holahan and Allison Cook, “Changes in Economic Conditions and Health Insurance Coverage, ,” Health Affairs web exclusive, November 1, All Adults <200% FPG 6.3 million 4.2 million 0.6 million 1.5 million 200%-400%400%+

Percentage-point Change in Sources of Coverage of Low Income Adults, 2000 to 2004 Source:John Holahan and Allison Cook, “Changes in Economic Conditions and Health Insurance Coverage, ,” Health Affairs web exclusive, November 1, 2005.

Percentage of Firms Offering Health Benefits, by Firm Size Source: Kaiser/HRET Survey of Employer-Sponsored Health Benefits, 2002 to 2005.

Family Health Insurance as a Percentage of Median Family Income Sources: Len Nichols, calculations based on data from the Kaiser Family Foundation, AHRQ and CPS.

Percentage-point Change in Sources of Coverage of Low Income Children, 2000 to 2004 Source:John Holahan and Allison Cook, “Changes in Economic Conditions and Health Insurance Coverage, ,” Health Affairs web exclusive, November 1, 2005.

Informing the Policymakers Or the Generally Interested Audience

Informing Policymakers and Informing the Debate  A key role that data plays is establishing a set of facts that inform policymakers of the landscape  Serves a critical educational role  Necessary precondition to “making your case”  Information can then be used by advocates on either side of the issue, with the general shared base of knowledge  From the perspective of how to inform policy with data: –Sets up a common level of education and understanding is important for the more complex discussions on specific issues  When thinking about framing of issues, can be useful to start with these common sets of agreement

A Few Examples

Health Spending is Highly Concentrated among a Relatively Few People Source: Berk and Monheit, “The Concentration of Health Care Expenditures, Revisited,” Health Affairs, March/April Expenditure estimates for civilian non-institutionalized population.

Health Care Spending as a Portion of the Gross Domestic Product Source: Centers for Medicare and Medicaid Services.

Increases in Health Insurance Premiums Compared to Other Indicators, * Estimate is statistically different from the previous year shown at p<0.05. † Estimate is statistically different from the previous year shown at p<0.1. Note: Data on premium increases reflect the cost of health insurance premiums for a family of four. Source: KFF/HRET Survey of Employer-Sponsored Health Benefits: ; KPMG Survey of Employer- Sponsored Health Benefits:1993, 1996; The Health Insurance Association of America (HIAA): 1988, 1989, 1990; Bureau of Labor Statistics, Consumer Price Index (U.S. City Average of Annual Inflation (April to April), ; Bureau of Labor Statistics, Seasonally Adjusted Data from the Current Employment Statistics Survey (April to April), %† 2.3% 2.2%

Medicaid Births as a Percentage of Total Births in the US, 1993 to Source: MCH Update 2002: State Health Coverage of Low Income Pregnant Women, Children, and Parents.

Informing the Debate  Example: Minnesota Governor’s proposal in 2003 to:  Eliminate General Assistance Medical Care  Reduce eligibility for subsidized health insurance coverage for single adults from 175% FPG to 75% FPG, and for parents from 275% FPG to 175% FPG  Hospital and provider concerns raised  Request from Governor’s office: What will be the resulting uncompensated care?

Methodology  Used a variety of state of Minnesota data sources including: –State health expenditure accounts –Hospital and provider reporting of uncompensated care –Enrollment and expenditure figures from our state Medicaid agency –Household insurance survey data –Published academic literature

Results: Estimated Impact on Number of Uninsured  Minnesota has relatively low uninsured rate: estimated at 5.7% at time of analysis  Number of uninsured increase by the following relative to current levels: –Baseline estimate, 2003: 272,000 total – 2004: +32,206 – 2005: +50,577 – 2006: +57,476 – 2007: +63,108

How Do These Estimated Increases in Uncompensated Care at Hospitals Compare to Current Levels? +34% +80% +88% +63% +28%

Making the Case

 Just as data can be useful in providing a common set of facts and also to frame an issue for policymakers, it can also be used to make a specific case about the effectiveness of programs  Following Example: –Borrowed in its entirety from :  Presentation by Deborah Florio, RI Department of Human Services  June 25, 2005  Presentation to Academy Health-Annual Research Meeting

How are Medicaid & SCHIP Weathering the Fiscal Storm in Rhode Island? Presentation to Academy Health-Annual Research Meeting By Deborah Florio RI Department of Human Services June 25, 2005

Protecting RIte Care From ‘Going Under’ Through Value Based Purchasing Ten year effort of using Research and Data Analysis to highlight successes and define improvement needs Document What We Do and Why It Matters Use of Descriptive Materials to “Tell the Story”

Rite Care Approach  Set Goals Early  Identify Measurable Indicators  Establish a Baseline  Implement Program Intervention  Monitor Trends  Evaluate Impact  Make Midcourse Corrections  Provide Concise Information to Target Audience

RIte Care Created in 1994 under Medicaid R&D waiver with the following goals: 1.Reduce uninsurance for low-income children and families 2.Improve access, service quality and health status for the covered population 3.Control the rate of growth in Medicaid expenditures for the eligible population

Goal 1 : Reduce uninsurance for low-income children and families

Percent Uninsured Rhode Island Children < 18 Years Old Data Source: Medicaid Research and Evaluation Project, RI Access Project US Bureau of the Census, Current Population Surveys (September estimates) 2nd1st2nd3rd1st3rd8th25th2nd

Goal 2: Improve access, service quality and health status for the covered population

Oversight and monitoring of Health Plan contracts site visits encounter data analysis Health Plan Performance incentives Trend access, quality and health outcome indicators for all enrollees RIte Care Member Satisfaction Survey Methods

Monitoring Trends

RIte Care Lead Screening Rates Improve Percent of Two Year Olds with Timely Recommended Screening *** * GAO report + NHANES estimates ** Patrick Vivier, MD, Phd, 1997

Infant Mortality Rate Declines in Rhode Island Infant Mortality by Insurance Status Data Source: Medicaid Research & Evaluation Project Center for Child & Family Health, Department of Human Services Linked Birth Death File , Division of Family Health, Department of Health (n=905) Deaths per 1000 births to Infants days – 3 year moving average

Goal 3: Control the rate of growth in Medicaid expenditures for the eligible population

RIte Care: Cost-Efficient RIte Care: Cost-Efficient “ A few states have revamped their organizational and management systems to ensure better access to medical care while keeping costs under control. Rhode Island stands out in this respect.” Governing Magazine, Feb 2004

Lessons Learned  Start Early to Establish a Baseline  Ensure Access to Data  Use the Data in a Variety of Ways (Speak to Legislature, Governor, Advocates, Grantors)  Integrate Research into the Medicaid Program  Use Interdisciplinary Team  Supplement Research and Evaluation with Outside Funding  Monitor Long Term Goals and Improvements

Developing the Solution and Assessing its effects  Finally, data can also be used to identify potential solutions to issues that have been raised and then to assess and monitor the effects of the solution  Example: Arkansas Childhood Obesity Initiative

2005 Statewide Results (Arkansas): BMI Classifications for All Students Healthy Weight 60% At Risk for Overweight 17% Overweight 21% Underweight 2%

Act 1220 Activities and Impact  Elimination of all vending machines in public elementary schools statewide  Requires professional education of all cafeteria workers  Public disclosure of “pouring contracts”  Assessment and development of physical activity and health education standards  Parent advisory committees for all schools  Child Health Advisory Committee  Annual BMI Assessment on every child AND:  Evaluation component through U of AR School of Public Health

In Summary:  Remember again that: –Legislators and policymakers are there to legislate and make policy –And will do so in the presence or absence of data –So, while the “plural of anecdotes is not data” sometimes “the plural of anecdotes can be legislation” –Data effectively used can help to inform the process

Contact Information Scott Leitz, Director Office of Health Policy and Research Minnesota Department of Health Phone: