ABSTRACT

An examination of models used to inform health reform in the United States. For this

paper I examined the technical appendix or methodology paper associated with six

major microsimulations used to inform the passage of the Patient Protection and

Affordable Care Act (PPACA) and to predict the effects of implementing the act’s

provisions. See Table 1 for a list of specific models. I focus on models used to

address access with an eye toward possible uses of improved models and

simulations to address quality.

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Introduction

“It is hard to ignore that in 2006, the United States was number one in terms of

health care spending per capita but ranked 39th for infant mortality, 43rd for adult

female mortality, 42nd for adult male mortality, and 36th for life expectancy. Health

expenditures in the United States neared $2.6 trillion in 2010, over ten times the $256

billion spent in 1980. i The rate of growth in recent years has slowed relative to the late

1990s and early 2000s, but is still expected to grow faster than national income over the

foreseeable future.ii Since 2002, employer-sponsored health coverage for family

premiums have increased by 97%, placing increasing cost burdens on employers and

workers.iii In total, health spending accounted for 17.9% of the nation’s Gross Domestic

Product (GDP) in 2010.iv “Despite having the most expensive health care system, the

United States ranks last overall compared to six other industrialized countries—

Australia, Canada, Germany, the Netherlands, New Zealand, and the United Kingdom—

on measures of health system performance in five areas: quality, efficiency, access to

care, equity and the ability to lead long, healthy, productive lives.”v These facts have

fueled a question now being discussed in academic circles, as well as by government and

the public: Why do we spend so much to get so little?”vi

Solutions to the health care crisis; succinctly described above, focus on

improving the triad of Cost, Quality, and Access. Major reform efforts have culminated

in the passage of The Patient Protection and Affordable Care Act of 2010 (PPACA), which

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primarily addresses the issue of access. Economic models and simulations that

predicted policy outcomes supported reform efforts. Quality is presently under

investigation and modeling may have a role to play in this segment of reform.

Simulation modeling is becoming increasingly popular as a format for

understanding problems as well as designing and predicting the effects of health care

policy. The ability to model and simulate policy changes to predict their effects with

accuracy could help effectively prioritize legislation. They say you don’t know what you

don’t know but perhaps understanding how models and simulations are limited could

help to focus research efforts by addressing those limitations. Simulation models have

been shown to provide “reasonably accurate estimates, with confidence bounds of

~30%”.vii I believe simulations could become an even more reliable tool in policy

analysis and design over time with improved economic paradigms and standardized

methods as collaboration among disciplines is enabled by technology. The economic

models that were used were limited to the current neoclassical paradigm. As health

economics and heterodox views emerge and are full of possibilities, modeling and

simulation remains tied to the neoclassical economic paradigms.

Model & Simulation Basic Structure

Models provide the framework for simulations that predict outcomes. Microsimulations

predict behaviors over time. The models I examined all used the same basic

methodology of defining a baseline model, running a simulation with and without

specific policies applied, then comparing outcomes.

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View

In order to achieve the desired view, the modeler will make certain decisions about the

parameters, data sources, behavioral assumptions and outputs. View is not just a

reflection of micro vs. macro, it is also defined by what is considered endogenous and

exogenous to the model based on relevancy to desired outputs. Parameters are the

measurable factors used to define the system – microsimulations use the most discrete

parameters. In most cases they are represented as decision-making units or “agents”

subject to behavioral assumptions – providers, payers (public and private), individuals or

households, and employers. Data source selection is based on available data and best fit

for the desired view as well. The data may not match the view being sought precisely;

therefore, some manipulation takes place to achieve the most accurate approximation.

Closed or Open

If a model is closed, the population at the start remains constant throughout the

simulation. If it’s open then the population changes as agents enter and depart the

system.

Static or dynamic

Dynamic means behaviors change over time in response to system changes. In a static

model, actors maintain the same behaviors over time.

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Outputs

Coverage, cost and economic impact are the main outputs in the models I examined.

Health, however, is not an output. Models used to support quality efforts would need

to include health status as an output.

Models Examined

Purposes

I examined the methodology or technical documents for six major microsimulations

used to support the access improvement aspects of health reform. I looked at one

developed by the CBO to analyze an array of public policy options involving health

insurance coverage in 2007.viii Also, COMPARE, developed by RAND in 2009 as a way of

projecting how households and firms would respond to health care policy changes

based on economic theory and existing evidence from smaller changes.ix Jon Gruber of

MIT developed GMSim in 2009 to predict the effect of health market interventions on

the movement of people and dollars within the US healthcare system.x Lewin Group also

developed HBSM as a platform for analyzing the impact of health reform proposals -

created to provide comparisons of the impact of alternative health reform models on

coverage and expenditures for employers, governments and households.xi CMS

developed OHRM in 2011, a model that simulates the impact of health reform legislative

provisions on both household and employer decision-making in regard to health

insurance coverage and health spending. Finally, the Urban Institute developed HIPSM

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in 2011, which estimates the cost and coverage effects of proposed health care policy

options.xii

Parameters, Data, & Variables

Populations

Each model represents the population using some combination of three available

sources: Current Population Survey Annual Social and Economic Supplement (CPS-

ASEC), survey of Income and Program Participation (SIPP), and Medical Expenditure

Panel Survey Household Component (MEPS-HC).

Employers

Employers are represented and defined using MEPS-HC, MEPS-IC (the Insurance

Component), or the Kaiser Family Foundation/HRET Employer Health Benefits Survey.

Cost & Prices

Premium Prices

Medical Spending and Premium Prices are not treated as a behavioral characteristic of

agents but are instead estimated using three data sources MEPS-HC, MEPS-IC and

KFF/HRET. The microsimulations use one of two methods to estimate baseline

premiums for ESI.xiii Many modelers simulate insurance premiums using information on

health care expenditures and applying an estimate of insurer loading costs.xiv Loading

cost calculations change over time and the details of those changes are not studied or

understood, although they are incorporated into some of the models to determine the

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price of insurance offered using averages. This is somewhat helped by the 80/20 or

85/15 provision of ACA provided no major loopholes emerge. Baseline premium

constructions for the microsimulation models are represented in Table 2 (borrowed

from Jean M. Abraham, Ph.D., University of Minnesota). National Health Expenditure

Accounts (NHEA) and MEPS are the most relied upon sources of data used to

understand expenditures. While this may be fine in the higher views, microsimulations

would be better served with information about behaviors at a more discrete level.

Agents and behaviors

The patient-consumer, a key decision maker in these models, is represented as an

individual or a household. A central behavior modeled is the take-up of insurance.

Utility-based microsimulations build on an economic framework of utility maximization -

each agent (decision making entity – individual, household, firm etc.) compares all

available health insurance options and selects the option that best serves them.

Elasticity based microsimulations build on data collected from household surveys and

describes individual behaviors in terms of the responsiveness of insurance participation

to changes in the price of coverage. Utility tends to be more discrete and less

dependent on past data but data are often adjusted in order to more closely match

currently accepted elasticity data. Which is better depends on the view and desired

outputs of the model.

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Coverage

The effect of the coverage mandate is often predicted using data from Massachusetts

(97% compliance).

Limitations

Accuracy

A review of history suggests, unsurprisingly, that modelers are most accurate when

estimating the effects of incremental reforms that affect relatively small proportions of

the population.xv Accuracy is essentially determined by the modelers’ sense of validity

or, likely, what is perceived to be the audience’s validity tolerance. In my conclusion, I

will point to possibilities for improving accuracy in the aggregate by using standard

methods of development and loosely coupled component models.

Behavioral Assumptions

Behavioral assumptions may be the most limiting characteristic of these models. The

economic models that have been used to inform solutions are limited in their ability to

understand and express human behaviors and evaluate social impacts.

Behaviors

In modeling, behaviors are represented as formulas or logic derived from historical data

combined with theories that assume “rational behavior”xvi - “A major challenge facing

effective—mathematical—modeling… is to develop models that can take into account…

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[agents'] capacity for self-modification according to internally constructed and defined

goals. Basically, the assumptions required for tractable mathematics steer models away

from the most important aspects of human behavior.”xvii A deeper understanding of

human behavior could improve the use of the utilityxviii concept but it would need to

solve the problem of measuring value independent of price.

Value

Measuring the values inherent in transactions is a particularly difficult problem for

economists. Alfred Marshall (the father of neoclassical economics) thought that "We

might as reasonably dispute whether it is the upper or the under blade of a pair of

scissors that cuts a piece of paper, as whether value is governed by utility or cost of

production".xix I think the problem in this statement is that value is not the outcome of

tension between cost of production and utility – that is simply the optimal price a

producer can charge a consumer. It may seem semantic, but the difference becomes

clearer when we attempt to improve quality in health care. What a person is willing and

able to pay only represents the dollar value to the producer or seller although the price

does reflect some aspect of value to the consumer. I think including the degree to

which the transaction improves an individual’s wellbeing provides a more complete

picture of the value to the consumer. Because people have different resources, how

much they are able to pay says little about how valuable a good or service is to that

person in absolute terms. There is lost value in things that the consumer needs but

cannot afford. The value to society is the net contribution to productivity and the

improved wellbeing of others as a result of the transaction. In other words, a single

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transaction has different values to different agents throughout the system. For

example, the picture is incomplete if a model accounts for uncompensated care

(unemployed and uninsured individuals who seek medical care for which they cannot

pay) as a cost to the provider but, not for the fact that health status and employment

are bi-directionally related and by not receiving care, the probability of employment and

ability to pay for future care declines. So, by providing effective care, a provider still

adds value to the individual, the system and potentially saves itself the cost of future

uncompensated care. If the goal of economics is simply to improve the financial returns

of producers then neoclassical economic approach to defining value makes sense. But,

especially when the product is health status, the real value has other dimensions to the

individual, household, and society writ large.

Utilization

Utilization is a key component to understanding cost growth. The relationship

between coverage and utilization is a point of contention among experts. Studies that

provide evidence of this relationship are limited primarily to the RAND Health Insurance

Experiment from 1971 and a few statistical analyses related to major changes in

Medicare and Medicaid.

One major limitation of the RAND HIE for the purposes of assessing PPACA is that it

did not include an uninsured group. Additionally, since the completion of the RAND HIE,

many changes have occurred that could affect behaviors, for instance:

Direct to consumer advertising of pharmaceuticals were made legal in 1985

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Reimbursement models have changed more than once

Managed care has peaked and faded

Tax treatment of private payers has changed

The Mental Health Parity Act of ’96 made psychiatric benefits more prevalent

The Balanced Budget Act of 97 further changed Medicare reimbursement

HIPPA, COBRA and CHIP

The rise of PPOs

Major hospital mergers

The Internet has become a more accessible source of information (affecting the

ability to maximize utility)

Applying policy changes effectively to agents in the 1971 regulatory environment

doesn’t include several changes that may affect the way those agents behave today.

Many statistical methods of updating consumer behavior have been used however,

qualitative analysis of individual perceptions of health insurance could lend a lot to the

effort to improve the effect of insurance plans on the quality of care.

Utilization in microsimulations appears to be corrolated mainly to coverage in a one-

way positive direction. (As coverage rises, utilization rises.) because the view is limited

to insurance coverage and the direct effects of PPACA related to coverage. Changing

one person’s health coverage will change an individual’s behavior; changing everyone’s

will also likely alter the demand facing providers and perhaps affect choices throughout

the health care system.xx

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Provider behavior is not detailed into these models even though reimbursement

structure and patient volume are thought to influence behavior and therefore

utilization. The CBO estimates that a 10% reduction in fees paid to physicians will

induce a 2.8% increase in service utilization in a 2007 background paper entitled Factors

Underlying the Growth in Medicare’s Spending for Physicians’ Services. Physician

behavior is not limited to response to reimbursement rates.

Defensive medicine is another behavior not included but, commonly thought to

influence utilization and therefore costs. Details of the relationship between public and

private reimbursement fees are unclear and may have an impact on provider behavior.

Provider decisions to participate in various networks and their market power to

negotiate dollar conversion factors are not well understood or represented. Finally,

physicians may vary in their commitment to patient outcomes based on other factors

that would be important to understand if we want to improve quality.

Employer behaviors are modeled in terms of the decision of employers to offer

health insurance. This decision too can be looked at in terms of elasticity of price or

utility. There is very little research about how employers make decisions regarding

offers of health insurance. There is some discussion about crowd out which is

considered by modelers in various ways. Some employers may be driven by a belief that

healthy employees are productive employees. Proving or disproving this and

quantifying it may be influential in improving quality.

Private payer behavior in the new (post PPACA) environment is almost a

complete unknown and may limit the capabilities of these models – particularly at the

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state level. Concerns about crowd-out cannot be reliably modeled, simulated or

predicted without more certainty about employer, provider, and patient-consumer

behaviors.

Unknowns

There are non-price factors that remain unknown without some comprehensive

qualitative studies of all agents involved. Environment is exogenous to these models.

Education is not considered to be variable but may play a prominent role in health

status as indicated by many studies.

Another potential future consideration lies in upcoming immigration reform’s

impact on uncompensated care. Forecasts of the Medicare program substantially

underestimated the cost per insured person because the enormous change in coverage

induced systemic effects on health spending such as new investments in hospitals. This

effect may not apply – the characteristics of the uninsured are unknown - including and

especially health status.

The effect of mental health and wellbeing on somatic health can play a role in

improving how we define quality.

The Future

Much expert attention is now focused on attempting to improve quality of care;

that is to say, outcomes and cost-effectiveness. The predominant belief is that

insurance reimbursement models have unintentionally caused over-utilization of

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unnecessary care, redundancies, and other inefficiencies. But, how to pay for outcomes

is difficult for the payer to conceptualize. In my opinion, the value of a dollar spent is

dependent on whose perspective you take and the circumstances surrounding the

transaction. Utility does not accurately capture the depth of this reality at the individual

level. Further, the measure of economic success of any entity should not be limited

soley to the accumulation of wealth but should include quality of life and social impact

elements as well. Modeling to include a variety perspectives is a future possibility.

“Complexity economics is the application of complexity science to the problems of

economics. It studies computer simulations to gain insight into economic dynamics, and

avoids the assumption that the economy is a system in equilibrium.”xxi There has been

"little work on empirical techniques for testing dispersed agent complexity models".xxii

The modeling process could be more transparent and collaborative if given a

common format and design principles. There are software design concepts and

principles that could be applied to model design and development that might facilitate

their maturation. There may be ways for simulations to be linked to live data.

The accuracy of aggregating and projecting functions may improve if modelers

were to consider using major principles of open source programming, and service

oriented architecture using a common modeling language for collaboration. Then they

could work on smaller, more discrete – (read: more accurate) interoperable (loosely

coupled) pieces. Those more accurate pieces could then be used in a larger, shared

environment or copied to a separate environment creating a flexible, sharable

environment. Rapid, even real-time, turn around may be possible. Imagine a library of

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agents and behaviors and data sets and a visual interactive environment where models

can be built and simulations can be run using compounds of other models.

I think any economic model that uses aggregate consumer data could be

redesigned to take the input of actual people. It would be quite interesting to compare

projected outcomes to outcomes produced by the participants. I would think it might

be particularly valuable in the limitations described of the Rand Health Insurance

Experiment. Consider conducting studies with people providing data directly to the

catalog. With more people working on a problem, more creative solutions can be

attained. Google, Mozilla, and Apple have successfully used these methods. To improve

data, the Maryland Exchange could include opt-in surveys or other such information

gathering techniques. There are many opportunities that might be worth exploring in

collaboration with computer scientists and qualitative researchers. The possibilities are

only limited by imagination.

Conclusions

These models have been useful despite the breadth of limitations identified. A strong

case can be made for further improving their usefulness and reliability with focused

research efforts, unified methods and collaborative development. The opportunities to

reconsider economic paradigms in the process of addressing the quality aspect of health

reform are huge. Prometheus gave fire & optimism to humans – with fire, they could be

optimistic; with optimism, they could use fire constructively, to improve the human

condition. “'Philanthropía'—loving what it is to be human—was thought to be the key

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to and essence of civilization.” xxiii I am optimistic that we have the tools we need to

achieve our potential and improve the human condition – models and simulations are

among those tools. New methods of evaluating the value of transactions are required to

effectively determine appropriate policy and market conditions that optimize the health

and well being of our citizens and modeling has a role to play in that process. All agents

have valuable insights and information and all perspectives are relevant. Modeling to

reveal the interrelationship between the micro and the macro; the various agents; and

the economic and social impacts are possibilities worth pursuing as we attempt to

improve the health of our citizens.

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Table 1

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Table 2

Table 2. Model Comparison: Baseline Premium Construction

ESI Premium Construction Non-Group Premium Construction

CBO Expected aggregate spending of a firm’s workers Actuarial values assigned based on firm size, income, health status 9% to 27% loading fee applied depending on firm size Incorporates state-specific information

Factor-based approach using information on age, sex, health, experience, and state 29% loading fee applied Includes state-specific information/adjustments

GMSIM (Gruber) _Individual-level cost index (age/sex/health rating) averaged over synthetic firm; index aligned to employer premium distribution to assign premium to firm _Actuarial value assigned based on income and firm size _Loading fee implicit in premium _Adjusted for state variation in premiums

_Age-health status spending distribution from MEPS applied to CPS _Loading fee applied with fixed (15%) and variable components; varying load component equal to 30% of average unloaded non-group cost, based on age interval _Includes state-specific information/adjustments

COMPARE (RAND) _Firm-specific premiums based on experience-rated and

community-rated estimates (former use predicted spending of workers and dependents, while the latter use 12 pools based on 4 census regions by 3 firm sizes) _Actuarial values assigned based on firm size _8.3% to 20% loading fee applied depending on firm size _No detailed information available about incorporation of state-specific information

_Age-health status risk pools to estimate spending from MEPS-HC for those reporting individual coverage _Loading fee applied (details not available) _Includes state-specific information/ adjustments (approximated)

HBSM (Lewin) _Use expenditures of workers and apply rating practices (e.g., small group market) to estimate premiums; premiums also estimated for self-funded plans _Actuarial values assigned based on comparison of employer plan to standard benefits _5.5% to 40% loading fee applied depending on firm size _State code imputed to MEPS and state small-group rating rules applied

_Predicted spending and rating practices (age, sex, health status) _40% loading fee applied _Includes state-specific information/adjustments

HIPSM (Urban Institute) _Built from risk pools from underlying health care costs;

blend of actual and expected costs _Actuarial value assigned based on firm size _Loading fee applied depending on industry and firm size _Accounts for state variation in spending

_Predicted spending among those in non-group market; model based on age sex, health status, and “typical” rating rules _Loading fee applied (details not available) _Includes state-specific

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information/adjustments

i Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Care Expenditures Data, January 2012. ii Robert Wood Johnson Foundation, High and rising health care costs: Demystifying U.S. health care spending, October 2008. iii Kaiser Family Foundation and Health Research and Educational Trust. Employer Health Benefits 2012 Annual Survey. September 2012.iv Martin, A.B. et al. January 2012. Growth in US health spending remained slow in 2010; Health share of gross domestic product was unchanged from 2009. Health Affairs 31(1): 208-219.v http://www.commonwealthfund.org/News/News-Releases/2010/Jun/US-Ranks-Last-Among-Seven-Countries.aspx vi This article (10.1056/NEJMp0910064) was published on January 6, 2010, at NEJM.org. http://www.nejm.org/doi/full/10.1056/NEJMp0910064 vii Glied, Sherry and Tilipman, Nicholas 2012. Simulation Modeling of Health Care Policy Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York 10032.viii CBO October 2007 Health Insurance Simulation Model: A Technical Descriptionix RAND Corporation 2006. The Health Insurance Experiment A Classic RAND Study Speaks to the Current Health Care Reform Debatex Gruber, Jonathan Microsimulation Estimates of the Effects of Tax Subsidies for Health Insurance Massachusetts Institute of Technology, National Tax JournalVol. Llll, No. 3, Part 1xi The Lewin Group 2009. The Health Benefits Simulation Model (HBSM): Methodology and Assumptionsxii Blumberg, Linda J. Shen, Yu-Chu Nichols, Len M. Buettgens, Matthew Dubay, Lisa C. McMorrow, Stacey 2003. The Health Insurance Reform Simulation Model (HIRSM): Methodological Detail and Prototypical Simulation Resultsxiii Abraham, Jean M. March 2012. Predicting the Effects of the Affordable Care Act: A Comparative Analysis of Health Policy Microsimulation Models from the State Health Reform Assistance Network. xiv Glied, Sherry and Tilipman, Nicholas 2012. Simulation Modeling of Health Care Policy Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York 10032.xv Zabinski D, Selden T, Moeller J, Banthin J. 1999. Medical savings accounts: microsimulation results from a model with adverse selection. J. Health Econxvi Rational choice theory, also known as choice theory or rational action theory, is a framework for understanding and often formally modeling social and economic behavior. Rationality, interpreted as "wanting more rather than less of a good", is widely used as an assumption of the behavior of individuals in microeconomic models and analysis and appears in almost all economics textbook treatments of human decision-making. It is also central to some of modern political science, sociology, and philosophy. It attaches "wanting more" to instrumental rationality, which involves seeking the most cost-effective means to achieve a specific goal without reflecting on the worthiness of that goal. - http://en.wikipedia.org/wiki/Rational_choice_theory xvii http://www.pnas.org/content/99/suppl.3/7288.short

xviii In economics, utility is a representation of preferences over some set of goods and services. Preferences have a utility representation so long as they are transitive, complete, and continuous. Utility is usually applied by economists in such constructs as the indifference curve, which plot the combination of commodities that an individual or a society would accept to maintain a given level of satisfaction. Individual utility and social utility can be construed as the value of a utility function and a social welfare function respectively. When coupled with production or commodity constraints, under some assumptions, these functions can be used to analyze Pareto efficiency, such as illustrated by Edgeworth boxes in contract curves. Such efficiency is a central concept in welfare economics. - http://en.wikipedia.org/wiki/Utility#Utility_as_probability_of_success

xix Marshall, Alfred Principles of Economics (1890)xx Remler DK, Zivin JG, Glied SA. 2004. Modeling health insurance expansions: effects of alternate approaches. J. Policy Anal. Manag.xxi http://en.wikipedia.org/wiki/Complexity_economics xxii Rosser, J. Barkley, Jr. "On the Complexities of Complex Economic Dynamics" Journal of Economic Perspectives, V. 13, N. 4 (Fall 1999): 169-192.xxiii http://nvs.sagepub.com/content/39/3/385