What, Why and How: Modeling to Address Health Policy Questions Deborah Chollet Senior Fellow, Mathematica Policy Research The Robert Wood Johnson Foundation’s.

Slides:



Advertisements
Similar presentations
Simulating Publicly Subsidized Reinsurance Strategies In Three States Lisa Clemans-Cope, Ph.D. (presenter) Randall R. Bovbjerg, J.D. (PI for Reinsurance.
Advertisements

The Role of Medicaid in a Restructured Health Care System Cindy Mann Executive Director Center for Children and Families Georgetown University Health Policy.
Household Projections for England Yolanda Ruiz DCLG 16 th July 2012.
2003 Alabama Health Care Insurance and Access Survey Montgomery, AL May 2, 2003 Ashley Alvord, MPH Alabama Department of Public Health Children’s Health.
The Uninsured: Policy and Data Issues Michael J. O’Grady, Ph.D. Assistant Secretary for Planning and Evaluation US Department of Health and Human Services.
State-Level Estimates for Tracking Health Reform Impact: Opportunities and Challenges Julie Sonier SHADAC SCI Annual Meeting August 4, 2010.
Overview of Health Care Coverage and Cost Trends in Minnesota Presentation to the State Budget Trends Study Commission April 22, 2008 Julie Sonier Director,
Introduction to STINMOD and Microsimulation Modelling in Australia Ben Phillips: Principal Research Fellow, NATSEM, 21 Feb 2015.
1 Healthcare Reform Medicaid Provisions and Opportunities Legal Action Center.
1 Revisiting the SCHIP Funding Formula AcademyHealth National Health Policy Conference State Health Research and Policy Interest Group Meeting Washington.
SEDS Macroeconomic Module Alan H. Sanstad, LBNL May 7, 2009.
SCAODA June 7th, 2013 Governor Walker’s Entitlement Reform & Patient Protection And Affordable Care Act (PPACA) 1.
Health Insurance Decisions, Expectations, and Job Turnover Randall P. Ellis Boston University and UTS-CHERE Albert Ma Boston University.
Congressional Budget Office Presentation to The Tax Policy Center and the American Tax Policy Institute Taxes and Health Insurance February 29, 2008.
A service of Maryland Health Benefit Exchange Health Care. Women of Color Get It September 8, 2012.
Oklahoma SoonerCare and the Affordable Care Act: Changes on the Horizon Buffy Heater, MPH Director of Planning & Development October 12,
Lessons from Medicaid Expansion in Arizona & Maine Tarren Bragdon, CEO Foundation for Government Accountability Naples, Florida
Improving Access In a Binational Population The Potential Role for Binational Health Insurance Tim Waidmann & Saad Ahmad The Urban Institute.
1 Public Employees Benefits Board 2006 Medical Procurement July 12, 2005 Richard Onizuka, Health Care Policy Washington State Health Care Authority.
Health Insurance for Utah Children and Small Businesses November 15, 2006 Expanding Health Insurance Coverage for Utah’s Uninsured Citizens.
Exhibit 1. Fifteen Million Young Adults Ages 19–25 Enrolled in or Stayed on Their Parents’ Health Plan in Past 12 Months Distribution of 15 million adults.
Center on Budget and Policy Priorities cbpp.org ACA Health Coverage Enrollment Overview Center on Budget and Policy Priorities September 24, 2013.
Exhibit ES-1. Synergistic Strategy: Potential Cumulative Savings Compared with Current Baseline Projection, 2013–2023 Total NHE Federal government State.
Use of a Simulation Model to Inform State Policy: The Case of New Jersey’s Non-Group Health Coverage Market Alan C. Monheit, Ph.D. Joel C. Cantor, Sc.D.
National Health Care Reform: Issues and Outlook James C. Capretta Fellow, Ethics and Public Policy Center Worldwide Employee.
Assessing the Value of the NHIS for Studying Changes in State Health Coverage Policies: The Case of New York Sharon K. Long John A. Graves Stephen Zuckerman.
HEALTH CARE REFORM UPDATE FOR AVOYELLES PARISH SCHOOL BOARD EMPLOYEES Prepared by: APSB Finance Department.
Using the National Health Interview Survey to Evaluate State Health Reform: Findings from New York and Massachusetts Sharon K. Long SHADAC/University of.
PROJECTIONS OF INCOMES, PENSIONS AND LONG-TERM CARE WORKPACKAGE 5.
Healthy NY NYS Insurance Department Health Bureau.
Options to Extend Health Coverage in Delaware. Key Background Observations n Preponderance of uninsured are working families with incomes between 100%
K A I S E R C O M M I S S I O N O N Medicaid and the Uninsured Figure 0 Robin Rudowitz Associate Director Kaiser Commission on Medicaid and the Uninsured.
Health Insurance Decision Tools for States Steven B. Cohen, Ph.D. Agency for Healthcare Research and Quality.
Adequate Health Care Task Force April, 2006 ®®. 2 A Plan for Illinois’ Working Uninsured Issue is Multi-Faceted –We need to address the working uninsured.
THE COMMONWEALTH FUND The 2009 Congressional Health Reform Bills: Insurance Coverage Sara R. Collins, Ph.D., Vice President Rachel Nuzum, M.P.H., Senior.
Data Used to Model Health Reform: The Health Benefits Simulation Model (HBSM) Presented to: 2009 APDU Annual Conference by: John Sheils, Vice President.
3 August 2006 State Coverage Initiatives Workshop for State Officials 1 Session 3: Coverage Mandates Panelist: Jerry Russo Department of Economics University.
Evaluation of Maine’s Dirigo Health Reform: Initial Experience and Lessons for other States February 1, 2008 Debra J. Lipson and James M. Verdier Mathematica.
Reforming Health Care: Making Sense of Health Care Finance amid Growing Underinsurance Leonard Rodberg, PhD Urban Studies Dept., Queens College/CUNY Prepared.
State and Local Efforts to Close the Gaps Between Public and Private Insurance Coverage Vickie S. Gates Vice President Academy for Health Services Research.
Copyright © 2011 by the American Academy of Actuaries Potential Approaches to Calculating Actuarial Value Cori E. Uccello, FSA, MAAA, MPP Senior Health.
Health Insurance and the Wage Gap Helen Levy University of Michigan May 18, 2007.
Consumer Response to a National Marketplace for Individual Insurance Stephen T Parente, Roger Feldman University of Minnesota October 19, 2008 Supported.
STATE HIGH RISK POOLS Deborah Chollet, Senior Fellow Mathematica Policy Research October 3, 2008.
Modeling Health Reform in Massachusetts John Holahan June 4, 2008 THE URBAN INSTITUTE.
Introducing PlanAdvisor™. PlanAdvisor At Commercial Insurance Services, we see a simplified way for you to approach the benefits plan design process.
Small Area (e.g. County-level) Estimates. Concepts Considerable interest in small area estimates of uninsured (e.g. County level) Two estimation methods.
Health Insurance Demand Responses from New Price Structures Offered by Consumer Directed Health Plans Stephen T Parente $,# Roger Feldman # Jean Abraham.
SustiNet Policy Options: Cost and Coverage Estimates SustiNet Partnership Board November 18, 2010 Stan Dorn Senior Fellow The Urban Institute.
SCI Reinsurance Institute, Albany Marriott 12 Sept ; slide 1 Garrett-Bovbjerg, Modeling Modeling & Other Consultation Presentation to Reinsurance.
THE COMMONWEALTH FUND Exhibit 1. Employer Coverage Continues to Be Major Source of Coverage for Employees of Larger Firms Percent of firms offering health.
Actuarial Research Corporation1 Inside the Black Box: Adjustments and Considerations for Public Policy Proposals AcademyHealth Annual Research Meeting:
Effects of the State Children’s Health Insurance Program on Children with Chronic Health Conditions Amy J. Davidoff, Ph.D. Genevieve Kenney, Ph.D. Lisa.
The Uninsured: What Do the New Numbers Mean for Health Reform? Alliance for Health Reform John M. Colmers, Secretary Maryland Department of Health and.
BilheimerView Graph # 1 KEY POLICY QUESTIONS FOR THE STATES What Is the Problem of Uninsured Kids in the State? What Are We Doing to Address That Problem?
Lynn A. Blewett, Ph.D. State Health Access Data Assistance Center University of Minnesota, School of Public Health November 10, 2004 Use of State and National.
Issues in Estimating the Coverage and Cost Impacts of Public Insurance Expansion John Holahan November 10, 2004.
State Child Buy-In Programs: A Snapshot Dawn Horner Georgetown Center for Children and Families Families USA January 30, 2009.
Developments in the estimation of the value of human capital for Australia Presented by Hui Wei Australian Bureau of Statistics Australian Bureau of Statistics.
K A I S E R C O M M I S S I O N O N Medicaid and the Uninsured New Models for Medicaid: A View from the Think-Tank Perspective Diane Rowland, Sc.D. Executive.
Effects of the State Children’s Health Insurance Program on Children with Chronic Health Conditions Amy J. Davidoff, Ph.D. Genevieve Kenney, Ph.D. Lisa.
The Uninsured in Virginia: An Update for the Virginia Health Care Foundation May 2016 Laura Skopec, Jason Gates, Michael Karpman, and Genevieve M. Kenney.
A Framework for Pension Policy Analysis in Ireland: PENMOD, a Dynamic Simulation Model T. Callan, J. van de Ven and C. Keane.
Congress Considers Major Medicaid Changes
What is PPACAcalc? PPACAcalc is a web based tool designed for insurance brokers PPACAcalc provides a customized employer impact study that will quantify.
Leslie E. Papke Michigan State University
Introducing PlanAdvisor™
How Will the Trump Administration’s Upcoming Rules Change the ACA Marketplaces in 2019, and What Will the Changes Mean for Consumers Who Rely on the Market.
Income as a percent of the federal poverty level
Presentation transcript:

What, Why and How: Modeling to Address Health Policy Questions Deborah Chollet Senior Fellow, Mathematica Policy Research The Robert Wood Johnson Foundation’s State Coverage Initiatives Program Washington, DC November 10, 2004

What is a Model? A structured way to think about a problem A structured way to think about a problem A way to measure responses and outcomes A way to measure responses and outcomes A way to compare alternative options A way to compare alternative options

Why Model Policy? Understand the impacts of policy change and sensitivity to program options Understand the impacts of policy change and sensitivity to program options –Program enrollment and cost –Uninsured population Understand the sensitivity of estimates to environmental factors Understand the sensitivity of estimates to environmental factors –Private insurance premium growth –Changes in employment –Demographic change

Types of Models  Determinate models e.g., Number enrolled = f (X 1... X n )+u = f (X 1... X n )+u Calculate population response to a specific change, all else held equal  “Cell-based” (spreadsheet) models (spreadsheet) models Calculate change by population subgroup, using average relationships  Microsimulation models Calculate change by individual using determinate- model relationships and other parameters

Determinate Models Estimate aggregate response to a program change Estimate aggregate response to a program change Support sensitivity analysis only by subgroups identified in the model Support sensitivity analysis only by subgroups identified in the model “Broad brush” approach, not suited to “fine-grained” analysis of complex systems and interactions “Broad brush” approach, not suited to “fine-grained” analysis of complex systems and interactions Offer a measure of precision of estimates, or a confidence interval Offer a measure of precision of estimates, or a confidence interval

Spreadsheet Model Mimics the operation of a program or system Mimics the operation of a program or system Incorporates average behavior by population subgroup Incorporates average behavior by population subgroup Relies on assumptions borrowed from populations that may differ in unmeasured ways Relies on assumptions borrowed from populations that may differ in unmeasured ways Outputs subgroup estimates only as defined in the model Outputs subgroup estimates only as defined in the model Relatively inexpensive and fast to assemble Relatively inexpensive and fast to assemble

Example: Simple Spreadsheet Model of the Dirigo Program Number of persons by subgroup Calculate subgroup offer and take up from available evidence Calculate financing specific to subgroups: State funds Federal match Employer contribution Individual premiums Sum across subgroups Sensitivity analyses

Microsimulation Model Mimics the operation of a program or system Mimics the operation of a program or system Operates on a large database (e.g., CPS) and outputs the same database with variables of interest calculated Operates on a large database (e.g., CPS) and outputs the same database with variables of interest calculated Incorporates partial responses to many variables at the level of the individual to calculate final response Incorporates partial responses to many variables at the level of the individual to calculate final response Supports relatively flexible analysis of subgroups and sensitivity to assumptions Supports relatively flexible analysis of subgroups and sensitivity to assumptions Often reveals results of complex logical relationships you might otherwise have overlooked Often reveals results of complex logical relationships you might otherwise have overlooked

Population input data file Create insurance families Apply parameters/relationship estimates to calculate individual take up probability Calculate stochastic estimate of take up Population output data file Subgroup and sensitivity analyses; refine actuarial estimates as needed Example: Simple Microsimulation Model of the Dirigo Program Constrain take up by eligibility rules Develop initial actuarial cost estimates

Choosing the Right Model How precisely can you identify what you need to know? How precisely can you identify what you need to know? How much detail do you need to know? How much detail do you need to know? How much time and budget do you have? How much time and budget do you have? What data are available to inform the model? What data are available to inform the model?

Designing the Model What are the key policy questions? What are the key policy questions? How many people will enroll? What woodwork effects? Change in program cost? What measures address the policy questions? What measures address the policy questions? Insurance family membership and income, actuarial factors (family type, family size, age, gender, location) What subgroups or other responses are of special interest? What program features might be changed? What subgroups or other responses are of special interest? What program features might be changed? Eligibility groups (parents, childless adults by income), impacts on linked programs

Structural Issues What program features and system relationships are important? What program features and system relationships are important? –Eligibility rules, outreach activities, income disregards –Funding caps or links to available funds What environmental variables should the model include? What sensitivity analyses do you want? What environmental variables should the model include? What sensitivity analyses do you want? –Aggregate cost growth –Changes in the industry, firm size, or wage structure of employment

Data Issues Do available data include the population of interest to you? Do available data include the population of interest to you? Are adequate estimates of behavioral response already available from the literature? Are adequate estimates of behavioral response already available from the literature? Can available data be “enhanced” to improve sample size and precision? Can available data be “enhanced” to improve sample size and precision?

Enhancing State Data for Modeling Merge population samples (e.g., 3 CPS years) Merge population samples (e.g., 3 CPS years) –Sample overlap –Data are observations of real residents, but trends may be lost “Balance” a national or regional sample “Balance” a national or regional sample –National/regional data are “raked” (re-weighted) to state totals –Data are synthetic, but reflect the most recent time period on key (control) variables

When Do You Need an Actuary? You do not need an actuary to estimate enrollment You do not need an actuary to estimate enrollment You do need an actuary to estimate per capita cost when: You do need an actuary to estimate per capita cost when: –Enrollee demographics are likely to change from past experience –The benefit design is new –The benefit design must be targeted to a cost cap

What Does an Actuary Need? Estimated eligibles and enrollment, by Estimated eligibles and enrollment, by –Individual age and gender –Family type and size –Geographic location –Benefit design option

Key Lessons for Modeling Policy Be clear and selective about what the model absolutely has to do Be clear and selective about what the model absolutely has to do Be pragmatic about structure Be pragmatic about structure If you choose microsimulation: If you choose microsimulation: –Modules are easier to build, debug, and update –Full integration can be costly and unnecessary –When possible, estimate model performance and benchmark

More Lessons Build into the model what you need out of it Build into the model what you need out of it –Variables for subgroup analysis –Parameters for sensitivity analyses Be aware of tautologies and power Be aware of tautologies and power –Are the results showing you only the input assumptions? –Do just a few observations drive the result?