RTH How could we do the most good - ReThink Health · 2016-08-17 · Work&in&Progress! ! How!could!we!do!the!most!good?!&&!Page!2!! that do not quickly pay for themselves are layered

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How$Could$We$Do$the$Most$Good$Here?$$$Using&the&ReThink&Health&Dynamics&Model&to&Evaluate&

&Combined&Intervention&Strategies&for&Local&Health&Systems&

Work&in&Progress!

Prepared by Jack Homer, PhD on behalf of the RTH Dynamics Team August 2013 $

The ReThink Health Dynamics Model is meant to help local leadership groups find a desirable

intervention strategy for improving the performance of their health systems. “Desirable” here

refers to an intervention strategy that meets the goals of the leadership group and is deemed

feasible in terms of cost and required implementation effort. The model simulates likely impacts

of proposed strategies along multiple outcome metrics, including health care costs, population

deaths, health inequity, and local economic productivity, out to the year 2040. A strategy

combining several different interventions may be necessary in order to achieve effective impact

across all of these outcome metrics simultaneously in a way that no single intervention can do.

The funding situation is an important consideration in identifying a desirable mix of

interventions. We have previously described how a community (“Anytown”) can be severely

constrained if all it has to work with is a one-time “innovation fund”; but how a mechanism of

“savings capture and reinvestment” can open up the possibility of action along multiple fronts

and substantially increase the combined impact (Homer, August 2013, “Introduction to the RTH

Dynamics Model”). With savings capture and reinvestment, the rapid cost savings from an

intervention like care coordination can become the engine that supports other interventions that

themselves are not rapidly cost saving but may offer other valuable benefits, like improving

health or reducing inequity. But that previous analysis also revealed that, even with savings

capture and reinvestment, funding can still become constrained when expensive interventions

Work&in&Progress! ! How!could!we!do!the!most!good?!&&!Page!2!!

that do not quickly pay for themselves are layered into the mix. Such overburdening of the

funding mechanism does not bring all progress to a halt, but it does have the effect of diverting

some funds away from other interventions, limiting their impact and also limiting the amount of

cost reduction that can generate savings for continued funding. In this way, the layering in of

any additional interventions that do not offer immediate cost savings of their own can be a

burden that, at least for some period of years, possibly causes overall health system performance

to become worse than it would have been without those additional interventions in place. Thus,

it is not in general the case that better outcomes can be achieved simply through the layering in

of more interventions; even with savings capture and reinvestment one must take care not to

spread available funds too thin.

We have also shown previously that the simulation-based ranking of individual

interventions along multiple outcome criteria depends somewhat on characteristics of the place

in question (Homer, August 2013, “Does Place Matter for Policy?”). These results helped us to

identify which interventions were likely most impactful, and in what sorts of places, but it did

not consider combined interventions, nor the role of funding limitations, nor how movement to a

new payment scheme (contingent global payments) or expansion of insurance eligibility under

ACA might affect the conclusions.

This bring us to the present analysis. Here we start by assuming the presence of savings

capture and reinvestment, so that several interventions may be combined in a powerful strategy

to address all outcome metrics of interest. The goal is to learn what the model can tell us about

which combined strategies are “best”, and how the selection of best might be affected by (a)

place (we again consider three: Anytown, Atlanta, and Morris County, New Jersey), and (b) four

possible scenarios of payment scheme and insurance eligibility.


Approach$to$Testing$

We developed a series of 24 simulations (base run + 23 intervention strategies) applied to each of

the three places, and for each of four scenarios for possible changes in payment scheme or

insurance eligibility. (Thus, a total of 24 x 3 x 4 = 288 simulations was performed.) This

breadth of testing was done to determine what combinations of interventions would be most

effective in terms of the model’s outcome metrics, and how the answer to that question might be

affected by choice of place or scenario.

As in previous analyses, all interventions are assumed to begin in 2012 and stay in place

through the end of the simulation in 2040. For all of these runs, it is assumed that 50% of any

cost savings against benchmarks for the insured population become available to the community

for reinvestment in the selected interventions.

We previously described, in “Does place matter for policy?”, the three places to which

the current version of the model (Version 2c; Reference Guide, Homer, May 2013) has so far

been calibrated. Anytown represents a national average, with relatively high prevalence fractions

of disadvantage, uninsurance, risky behavior, crime, and environmental hazards; perhaps

surprisingly high for a modern, developed nation. Atlanta has an identical fraction of

disadvantage but even more uninsurance, and a higher age-standardized death rate and

disadvantaged fraction of deaths. Morris County, in contrast, has low prevalence fractions of all

risk factors, and its age-standardized death rate and disadvantaged fraction of deaths are

accordingly low as well.

The four scenarios (S) defined by payment scheme and insurance eligibility are as

follows:


(S1) CGP0, ACA0: Continued fee-for-service payment; no expansion in insurance

eligibility;

(S2) CGP1, ACA0: Transition to Contingent Global Payment (CGP) starts in 2012 and

covers almost all of the insured population by 2016; no expansion in insurance eligibility;

(S3) CGP0, ACA1: Continued fee-for-service payment; expansion of insurance eligibility

causes two-thirds reduction of uninsurance starting in 2014 and is complete by 2016, as has been

projected to occur under full implementation of Affordable Care Act (ACA);

(S4) CGP1, ACA1: Transition to CGP; and expansion of insurance eligibility.

The$23$Intervention$Strategies$

The 23 intervention strategies are a sequence of model runs testing various combinations of

interventions for improving health care or for reducing health risks. In “Does Place Matter for

Policy?”, each of the 21 intervention types was tested individually. But, once one starts to

consider combinations, the potential number of multi-pronged strategies (two at a time, three at a

time, etc.) grows quickly into the thousands, and it is not practical to test them all. To limit the

task to something feasible and meaningful, we reviewed the results of our previous analyses and

decided to focus on the most promising interventions, eliminating some interventions from

consideration. This left us with ten initiatives (with one of them, called PrimaryCare, actually

comprising three separate interventions grouped as a single initiative) to be tested in various

combinations. These ten initiatives (I)—with descriptions, main effects, and relative intervention

cost—are as follows:

(I1) Coordination: includes technology assessment updating and also subsumes generic

drugs and shared decision making interventions—reduces health care costs; has moderate

intervention cost;


(I2) Behavior: healthier behavior intervention applied to all demographic groups—

reduces costs and deaths and inequity; has high intervention cost;

(I3) Environment: environmental hazards intervention applied to all demographic

groups—reduces costs and deaths and inequity; has high intervention cost;

(I4) PreScreen: pre-visit consultation intervention—reduces costs; has moderate

intervention cost;

(I5) Discharge: post-discharge care intervention—reduces costs; low intervention cost;

(I6) SelfCare: self-care intervention—reduces deaths and inequity, but may raise costs;

has high intervention cost;

(I7) PrimaryCare: combines preventive/chronic care intervention, PCP efficiency

intervention, and recruit PCP (FQHC) intervention—reduces deaths, but may raise costs and

inequity; has moderate intervention cost;

(I8) HospInfect: hospital-acquired infections intervention—reduces costs and deaths, but

may indirectly raise inequity; has low intervention cost;

(I9) FamilyPath: family pathways anti-poverty intervention—reduces inequity and raises

economic productivity; has very high intervention cost;

(I10) MentalDisadv: mental illness care intervention targeted to the disadvantaged—

reduces inequity and raises economic productivity; has high intervention cost.

The sequence of 23 strategies starts with Coordination (I1) alone, which is the single

most effective and rapid intervention for reducing health care costs, generating cost savings that

can be reinvested for funding other interventions. Behavior (I2) is then layered in: it also

reduces health care costs in both the short and longer term, and is one of the most important

interventions with regard to deaths, inequity, and economic productivity; Coordination plus

Behavior (“CB”) becomes the foundation for all strategies to follow. Environment (I3) is similar


in its effects to Behavior, though not quite as cost-effective; eleven of the 23 tested strategies

include Coordination plus Behavior plus Environment (“CBE”).1

Other initiatives that rapidly reduce health care costs are PreScreen (I4) and, somewhat

less powerfully, Discharge (I5). The fact that these two are of low or moderate cost to

implement means that they can be layered into a package of interventions without worrying that

they will constrain and dilute funds. Accordingly, PreScreen with or without Discharge appears

in eleven of the 23 tested strategies.

Combinations of the first five initiatives (I1 to I5) provide the cost savings that make it

possible to invest in other initiatives that are not cost saving (at least in the shorter term) but

provide other value. One of the most important of these is Self-Care (I6), which is relatively

expensive to implement and would by itself raise health care costs, but is (after Behavior) the

single most important intervention for reducing deaths, and is also important for reducing health

inequity and raising economic productivity. (In “Does Place Matter for Policy?”, we saw that

this importance of Self-Care held true in all three locations.). This initiative appears in sixteen of

the 23 tested strategies; in eight cases as adjunct to the CB combination (creating “CBS”), and in

eight cases as adjunct to CBE (creating “CBES”).

The remaining four initiatives may be useful when used as adjuncts to CBS or CBES.

PrimaryCare (I7) and HospInfect (I8) can help to further reduce the death rate, while

FamilyPath (I9) and MentalDisadv (I10) can help to further reduce inequity and increase

productive value.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 The importance of interventions to reduce behavioral and environmental risks, as part of a strategic package for

health system reform, has been been demonstrated previously through system dynamics modeling at the national level (Milstein et al. 2010; Milstein et al. 2011).


Each of the 23 intervention strategies tested includes no more than six of the ten

initiatives listed above. This limit was set in part for the sake of simpler analysis, but also on the

premise that it would be difficult for a local group of leaders to commit to a far-reaching strategy

that contains more than six components, from the standpoint of ongoing project management and

oversight, even if funding were adequate to do so. The management of even six concurrent

initiatives may be unrealistic for some locales; and indeed, when the RTH Dynamics model is

used in local workshops, an effective limit of fewer than six of the above initiatives is often set.2

Nonetheless, six initiatives may be what it takes to make a substantial impact across all outcome

metrics, and it is useful to ask how such a broadly effective approach can best be configured.

Numerical$Results$

Numerical results of testing are presented in Tables 1 to 4, one table for each of the four assumed

scenarios. For each of the three simulated places, four cumulative outcome metrics (2012

through 2040) are presented for the scenario’s base run: Average healthcare costs per capita;

Average death rate per thousand (age standardized); Disadvantaged fraction of deaths (a measure

of inequity); and Cumulative productive value of the employed population (in billions of

dollars). (These same metrics were reported in “Introduction to the RTH Dynamics Model” and

“Does Place Matter for Policy?”) The remainder of each column reports, for each of the 23

tested intervention strategies, the percentage change from the base run result, with one decimal

point of precision. A green shaded cell indicates the best (or tied for best) result among all

strategies along a particular metric. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2 For the present analysis, “capture savings” is a given for all runs and does not count as an initiative. “CGP” also does not count as an initiative here, but is rather an exogenous scenario condition present in half of the simulations performed. In local workshops to date, these two conditions have usually been described as interventions rather than as givens. If six interventions are allowed in such a workshop, then a team selecting “capture savings” and “CGP” would have only four additional allowed interventions.


Before considering what these results suggest about possible best strategies across

multiple metrics simultaneously, it is useful to look at the results one metric at a time. Here are

some observations on the varied impacts in Tables 1 to 4:

1. For all places and scenarios, the most important initiatives for healthcare cost reduction

are Coordination and Behavior, after which Environment, PreScreen, Discharge, and HospInfect

also contribute but by smaller amounts.

2. CGP reduces healthcare costs in the base run and also magnifies the impact of

Coordination on cost reduction. Because CGP is implemented starting in 2012, and healthcare

costs are assumed to be benchmarked to 2010, the additional healthcare cost reduction from CGP

translates into more cost savings available to the community for reinvestment. The additional

funds generated by CGP can make it easier to implement certain costly initiatives, such as

FamilyPath, MentalDisadv, and PrimaryCare, without diverting funds from other initiatives.

Thus, CGP makes it possible to include more initiatives in a combined strategy without causing

adverse effects due to spreading funds too thin.

3. Absent CGP, insurance expansion slightly magnifies the impact of cost reduction

initiatives, thereby enabling more cost saving and more funds available for reinvestment. With

CGP, this impact of insurance expansion disappears.

4. For all places and scenarios, the most important initiative for death reduction is

Behavior, followed by Environment, SelfCare, and HospInfect. The PrimaryCare initiative can

also reduce deaths significantly, but only when the payment scheme is CGP. CGP generates

more reinvestment funds to help with the implementation of PrimaryCare, as noted above, and it

also provides greater incentive to physicians to comply with preventive and chronic care


guidelines and to improve the efficiency of their practices. Absent CGP, the addition of

PrimaryCare to the CBS strategy can have neutral or even adverse marginal impact on deaths,

possibly doing more harm (spreading limited funds too thin) than good.

5. The initiative with the greatest potential for inequity reduction and productivity

improvement is FamilyPath, a potential that is, however, only fully realized in the presence of

CGP. Absent the additional cost savings generated by CGP, the high implementation cost of

FamilyPath leads to a shortage of funds, thereby blunting its impact on inequity and productivity

and doing more harm than good with regard to healthcare costs and death rate.

6. Aside from FamilyPath under CGP, the most important initiative for inequity

reduction is Behavior, followed by MentalDisadv, SelfCare, and Environment. The

MentalDisadv initiative, like FamilyPath, is specifically targeted to the disadvantaged, fully

aimed at reducing inequity. The Behavior, SelfCare, and Environment initiatives address all

people who can benefit from them, but because disadvantaged people tend to need more such

help than advantaged people do, these initiatives also have the effect of reducing inequity.

7. Aside from FamilyPath under CGP, the most important initiative for productivity

improvement is Behavior, followed by SelfCare, MentalDisadv, HospInfect, and Environment.

The MentalDisadv initiative increases productivity primarily by reducing absenteeism and

presenteeism due to mental illness; the SelfCare and Environment initiatives do so by preventing

severe chronic illness and death; the HospInfect initiative does so by preventing death.

$ $


Best$Strategy$Candidates$Based$on$Pareto$Efficiency$

If the results presented in Tables 1 to 4 pointed to one strategy that had top results across all four

outcome metrics, then the choice of best strategy would be clear. But such a fully dominant

strategy does not exist among the 23 strategies tested under any of the scenarios. One strategy is

consistently best for reducing healthcare costs (CBE + PreScreen + Discharge), but this strategy

is somewhat inferior along the other three metrics because it lacks key components, such as

SelfCare or MentalDisadv, that improve those metrics. But adding such components would

detract from the strategy’s cost-cutting ability, either by raising cost directly (as SelfCare does)

or by spreading funds too thin and forcing diversion of effort (as MentalDisadv does).

The field of multiple-criteria decision making (MCDM) addresses itself to such problems

in which there is no single strategy or option that is dominant across all outcome criteria of

interest. Qualitative and quantitative MCDM techniques are well developed and frequently used

for real-life decision problems. Well-known examples of such techniques include the Analytic

Hierarchy Process (Saaty 1980; Forman and Gass 2001; Bhushan and Rai 2004), Multiattribute

Utility Analysis (Keeney and Raiffa 1980), and Interactive Multiobjective Optimization

(Miettinen et al. 2008).

These MCDM techniques all start with the identification of a subset of strategies known

as Pareto efficient or non-dominated strategies. A Pareto efficient strategy is one for which no

other strategy under consideration dominates it—i.e., that is superior (or in a tie) with it across

all criteria of interest. The next step, after identifying the Pareto efficient strategies, is to elicit

preferences from the real-world decision makers that allow a single strategy to be selected as

“best”. This may be done most simply by presenting the strategies directly to the decision

makers and hoping they can reach a conclusion through discussion and voting; or, rather (as all


the formal techniques do) by asking various hypothetical choice questions that allow a

computable inference to be drawn about which of the Pareto efficient strategies should, in theory,

be most preferred by the decision makers.

Table 5 presents the subset of Pareto efficient (non-dominated) strategies, among the 23

strategies tested, for each combination of place and scenario. These were identified by pairwise

comparison of strategies with respect to the simulated outcomes (percentage change relative to

the base run, with single decimal point precision) reported in Tables 1 to 4. Pareto efficient

strategies are marked in Table 5 with a plus (+) sign. Green shading indicates a Pareto efficient

strategy that additionally is best (or tied for best) among all Pareto efficient strategies along at

least one outcome metric. Here are some observations from Table 5 (informed also by the

details in Tables 1 to 4):

1. Of the 23 strategies, 14 are Pareto efficient for at least one combination of place and

scenario, and two (CBE + PreScreen + Discharge, and CBES + Prescreen + Discharge) are

Pareto efficient for all 12 combinations of place and scenario. Four other strategies are Pareto

efficient for most combinations of place and scenario.

2. All 14 of the strategies that are ever Pareto efficient include both the Coordination and

Behavior (CB) initiatives, 12 include SelfCare (as part of CBS), 9 include PreScreen, 8 include

Environment (as part of CBE or CBES), 5 include Discharge, 4 include HospInfect, and 3

include MentalDisadv.

3. Most of the strategies that are ever Pareto efficient are well balanced with respect to

the four outcome metrics, but there is one notable exception. In the absence of CGP (Scenarios 1

and 3), the CBS + FamilyPath strategy overburdens the funds available and for many simulated


years constrains investments that can reduce healthcare costs and deaths. The reason that this

strategy is nonetheless Pareto efficient in 5 of the 6 columns for Scenarios 1 and 3 (all but

Atlanta-Scenario 1) is that its contribution to productive value (by gradually moving people from

disadvantage to advantage and more gainful employment) is still superior to that of any other

strategy, despite the budget-constrained path in getting there.

4. There are from 6 to 9 Pareto efficient strategies for any given combination of place

and scenario. Absent CGP (under Scenarios 1 and 3), the PrimaryCare initiative does not appear

in any of the Pareto efficient strategies, and the MentalDisadv initiative also sometimes falls out

of contention. With CGP (under Scenarios 2 and 4), sufficient funds are generated so that the

inherent value tradeoffs among the initiatives (cost vs. death vs. inequity and productivity) are

allowed to emerge without being masked by problems of insufficient funds. In Anytown and

Atlanta, where there are significant environmental hazards, this means that all four of the CBES

+ PreScreen–based initiatives are Pareto efficient. In Morris, where environmental hazards are a

lesser concern, this means that all seven of the CBS + PreScreen-based initiatives (that is, all of

the combined approaches using six interventions) are Pareto efficient.

Possible$Extensions$

The analysis here contributes to our understanding of intervention strategy for local health

systems, and puts the ReThink Health modeling team in a position to provide further-reaching

and more definitive answers than we have given previously. Nonetheless, there is still more

work that could be done along a few basic fronts:


1. The analysis here culminated with the identification of several possible candidates for

best strategy for any given combination of place and scenario, rather than identifying a single

winner. That last step would require the elicitation of preferences either by vote of decision

makers or through a formal quantitative technique of multicriteria optimization.

2. Only 23 interventions strategies were considered, a subset of the many strategies

possible. Not included at all in these strategies were the interventions of Crime Reduction,

Hospice, Malpractice Reform, Medical Home, Recruit PCP (General), and Student Pathways.

Further testing might include some of these interventions selectively to see whether they might,

in some instances, lead to additional Pareto efficient strategies. For example, perhaps Crime

Reduction could be part of a Pareto efficient strategy in Anytown, where crime is relatively high.

Or, perhaps Malpractice Reform could be part of a Pareto efficient strategy in Morris, where it

was one of the top interventions for reducing healthcare costs in the “Does Place Matter for

Policy?” analysis.

3. The analysis here considered only the three places to which the current version of the

model has so far been calibrated. When the model is calibrated to additional places, this sort of

strategic analysis should be extended to those places as well.

4. All analyses done to date have been deterministic, assuming baseline values for all

intervention-related constants: effect sizes, time constants, and implementation costs. A further

step could be to subject all identified Pareto efficient strategies to probabilistic Monte Carlo

testing to establish confidence intervals around each outcome metric. This would involve

sampling from probability distributions using the “min” and “max” values for the intervention-

related constants specified in the model’s Reference Guide (Table 3).


References$

Bhushan N, Rai K. Strategic Decision Making: Applying the Analytic Hierarchy Process. Springer-Verlag: London, 2004.

Forman EH, Gass SI. The analytical hierarchy process—an exposition. Operations Research 2001; 49(4): 469–486.

Homer J. ReThink Health: a simulation model of health system transformation. Reference guide for model version 2c, “Anytown, USA” calibration. For Fannie E. Rippel Foundation, Morristown, NJ; May 2013.

Homer J. Introduction to the ReThink Health Dynamics model: simulating local health reform in “Anytown USA”. For Fannie E. Rippel Foundation, Morristown, NJ; August 2013.

Homer J. Does Place Matter for Policy? The effect of local characteristics on intervention priorities in the ReThink Health Dynamics model. For Fannie E. Rippel Foundation, Morristown, NJ; August 2013.

Keeney R, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Wiley: New York, 1980.

Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multiobjective optimization: interactive approaches. Lecture Notes in Computer Science 2008; 5252: 27-57. Chapter 2 in Multiobjective Optimization: Interactive and Evolutionary Approaches, eds. Branke J, Deb K, Miettinen K, Slowinski R; Springer, 2008.

Milstein B, Homer J, Briss P, Burton D, Pechacek T. Why behavioral and environmental interventions are needed to improve health at lower cost. Health Affairs 2011; 30(5). DOI: 10.1377/hlthaff.2010.1116.

Milstein B, Homer J, Hirsch G. Analyzing national health reform strategies with a dynamic simulation model. Am J Public Health 2010; 100(5):811-819.

Saaty TL. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill: New York, 1980.

!Work&in&Progress! ! Title!&&!Page!15!!!

Table 1. Comparison of 23 intervention strategies along four cumulative outcome metrics through 2040, assuming continued fee-for-service payment scheme with no expansion of insurance eligibility (Scenario 1) Green shaded cell indicates best (or tied for best) result among all strategies along a particular metric.

Anytown Atlanta Morris Anytown Atlanta Morris Anytown Atlanta Morris Anytown Atlanta MorrisBase Run (Scenario 1) $9,535 $9,173 $9,215 7.543 7.946 6.185 46.6% 58.2% 21.8% $192.5 $1,110.2 $681.8

CARE COORDINATION, HEALTHIER BEHAVIORS, AND ENVIRONMENTAL SAFETY

Coord (with assessment updating) -4.0% -4.1% -5.3% -0.1% -0.2% -0.1% -0.5% -0.5% -0.7% 0.3% 0.4% 0.1%

Coord + Behavior (CB) -7.6% -7.7% -8.1% -6.4% -6.5% -5.6% -2.0% -2.1% -2.1% 1.4% 1.5% 0.6%

Coord + Behavior + Environment (CBE) -8.4% -8.2% -8.3% -8.7% -8.1% -5.9% -2.8% -2.6% -2.3% 1.6% 1.6% 0.6%

FURTHER FOCUS ON COST

CB + PreScreen -8.4% -8.7% -8.7% -6.4% -6.5% -5.6% -2.0% -2.2% -2.1% 1.4% 1.5% 0.6%

CB + PreScreen + Discharge -8.9% -9.2% -9.0% -6.4% -6.5% -5.5% -2.1% -2.2% -2.2% 1.5% 1.6% 0.6%

CBE + PreScreen -9.2% -9.2% -8.8% -8.7% -8.1% -5.8% -2.9% -2.7% -2.3% 1.6% 1.6% 0.7%

CBE + PreScreen + Discharge -9.7% -9.7% -9.1% -8.7% -8.0% -5.8% -2.9% -2.8% -2.4% 1.7% 1.7% 0.7%

FURTHER FOCUS ON HEALTH

CB + SelfCare (CBS) -5.8% -5.6% -6.9% -9.8% -9.7% -8.5% -3.2% -2.9% -4.0% 1.6% 1.6% 0.8%

CBS + PrimaryCare -5.4% -5.3% -5.6% -10.2% -9.6% -7.5% -2.5% -2.2% -2.4% 1.6% 1.4% 0.6%

CBS + HospInfect -6.1% -5.8% -7.1% -11.3% -11.0% -10.0% -3.1% -2.5% -3.8% 1.8% 1.7% 0.9%

CBE + SelfCare (CBES) -6.4% -5.9% -6.9% -11.0% -10.4% -8.2% -3.7% -3.2% -4.0% 1.7% 1.6% 0.8%

CBES + PrimaryCare -5.8% -5.5% -5.4% -11.0% -9.9% -7.1% -2.9% -2.4% -2.3% 1.5% 1.4% 0.6%

CBES + HospInfect -6.7% -6.1% -7.1% -12.5% -11.7% -9.8% -3.6% -2.8% -3.8% 1.8% 1.7% 0.9%

FURTHER FOCUS ON EQUITY & PRODUCTIVITY

CBS + FamilyPath -4.0% -3.4% -5.4% -3.3% -2.4% -3.7% -3.5% -2.3% -5.2% 2.2% 1.6% 1.0%

CBS + MentalDisadv -5.3% -4.8% -6.5% -9.3% -8.6% -7.9% -3.9% -3.4% -4.8% 1.9% 1.8% 0.8%

CBES + MentalDisadv -5.8% -5.1% -6.5% -10.3% -9.1% -7.6% -4.3% -3.6% -4.7% 1.9% 1.7% 0.8%

COMBINED APPROACHES USING SIX INTERVENTIONS

CBS + PrimaryCare + PreScreen + Discharge -6.7% -6.7% -6.3% -10.5% -9.8% -7.7% -2.8% -2.4% -2.6% 1.7% 1.6% 0.7%

CBS + HospInfect + PreScreen + Discharge -7.4% -7.1% -8.0% -11.3% -9.9% -9.9% -3.3% -3.1% -3.9% 1.9% 1.7% 0.9%

CBS + MentalDisadv + PreScreen + Discharge -6.6% -6.3% -7.3% -9.6% -9.0% -8.0% -4.2% -3.7% -4.9% 2.0% 1.9% 0.8%

CBES + PrimaryCare + PreScreen -6.4% -6.1% -5.7% -10.9% -9.8% -6.8% -2.9% -2.4% -2.2% 1.6% 1.4% 0.6%

CBES + HospInfect + PreScreen -7.4% -7.0% -7.6% -12.5% -11.6% -9.6% -3.7% -2.9% -3.8% 1.9% 1.8% 0.9%

CBES + MentalDisadv + PreScreen -6.5% -5.8% -6.9% -10.3% -9.0% -7.4% -4.3% -3.6% -4.6% 1.9% 1.8% 0.7%

CBES + PreScreen + Discharge -7.7% -7.4% -7.7% -11.3% -10.6% -8.2% -4.0% -3.5% -4.1% 1.8% 1.7% 0.8%

Percent change from Base Run

Death Rate per 1,000 stdized. Inequity (disadv. death %) Productive Value ($ billion)Healthcare costs per capita


Table 2. Comparison of 23 intervention strategies along four cumulative outcome metrics through 2040, assuming transition to Contingent Global Payment scheme with no expansion of insurance eligibility (Scenario 2)

Green shaded cell indicates best (or tied for best) result among all strategies along a particular metric.







CB + PreScreen -12.3% -12.3% -12.2% -6.5% -6.6% -5.6% -2.6% -2.8% -2.6% 1.8% 1.9% 0.7%


CBE + PreScreen -13.3% -13.0% -12.4% -9.0% -8.3% -5.9% -3.5% -3.4% -2.9% 2.0% 2.0% 0.7%



CB + SelfCare (CBS) -9.5% -9.3% -10.5% -11.0% -11.6% -9.6% -4.3% -4.1% -5.2% 2.1% 2.1% 0.9%

CBS + PrimaryCare -9.4% -9.5% -9.9% -13.0% -13.6% -11.5% -4.1% -4.0% -4.8% 2.3% 2.3% 1.0%

CBS + HospInfect -9.8% -9.4% -10.7% -12.2% -12.6% -10.8% -4.2% -3.8% -5.0% 2.2% 2.2% 1.0%



CBES + HospInfect -10.7% -10.1% -10.8% -14.3% -14.1% -11.0% -5.0% -4.3% -5.2% 2.4% 2.4% 1.1%


CBS + FamilyPath -9.3% -9.0% -10.4% -9.5% -9.4% -9.1% -10.0% -9.1% -11.8% 5.7% 5.6% 2.2%














Table 3. Comparison of 23 intervention strategies along four cumulative outcome metrics through 2040, assuming continued fee-for-service payment scheme with expansion of insurance eligibility (Scenario 3) Green shaded cell indicates best (or tied for best) result among all strategies along a particular metric.



Coord (with assessment updating) -4.9% -5.4% -5.6% -0.1% -0.2% 0.0% -0.6% -0.6% -0.7% 0.4% 0.5% 0.1%




CB + PreScreen -8.9% -9.7% -8.7% -6.3% -6.6% -5.4% -2.1% -2.2% -2.1% 1.3% 1.5% 0.6%


CBE + PreScreen -9.6% -10.2% -8.8% -8.5% -8.1% -5.6% -2.9% -2.8% -2.3% 1.5% 1.5% 0.6%



CB + SelfCare (CBS) -6.5% -6.8% -7.0% -9.8% -9.9% -8.0% -3.5% -3.1% -3.9% 1.6% 1.6% 0.7%

CBS + PrimaryCare -6.1% -6.6% -5.3% -10.6% -10.3% -6.4% -2.9% -2.6% -2.0% 1.6% 1.5% 0.6%

CBS + HospInfect -6.8% -7.0% -7.3% -11.1% -11.1% -9.6% -3.3% -2.7% -3.7% 1.7% 1.7% 0.9%



CBES + HospInfect -7.3% -7.3% -7.3% -12.5% -11.8% -9.2% -3.8% -3.1% -3.7% 1.8% 1.7% 0.8%


CBS + FamilyPath -5.6% -5.0% -5.3% -5.0% -3.1% -3.2% -5.4% -3.1% -4.7% 3.3% 2.1% 0.9%














Table 4. Comparison of 23 intervention strategies along four cumulative outcome metrics through 2040, assuming transition to Contingent Global Payment scheme with expansion of insurance eligibility (Scenario 4)

Green shaded cell indicates best (or tied for best) result among all strategies along a particular metric.







CB + PreScreen -12.2% -12.4% -12.0% -6.4% -6.6% -5.4% -2.6% -2.7% -2.5% 1.6% 1.7% 0.7%


CBE + PreScreen -13.2% -13.1% -12.2% -8.8% -8.3% -5.6% -3.5% -3.3% -2.8% 1.8% 1.8% 0.7%



CB + SelfCare (CBS) -9.4% -9.4% -10.4% -10.8% -11.5% -9.4% -4.3% -4.0% -5.3% 1.9% 1.9% 0.9%

CBS + PrimaryCare -9.3% -9.7% -9.6% -12.9% -13.6% -11.2% -4.2% -3.9% -4.7% 2.1% 2.1% 1.0%

CBS + HospInfect -9.6% -9.5% -10.6% -12.0% -12.6% -10.6% -4.2% -3.7% -5.0% 2.0% 2.1% 1.0%



CBES + HospInfect -10.5% -10.2% -10.8% -14.1% -14.1% -10.8% -5.0% -4.3% -5.2% 2.2% 2.2% 1.0%


CBS + FamilyPath -9.4% -9.1% -10.2% -9.3% -8.8% -8.8% -9.8% -8.4% -11.6% 5.4% 5.1% 2.2%














Table 5. Pareto efficient (non-dominated) strategies for the four scenarios Plus (+) sign indicates a Pareto efficient strategy among the 23 considered. Green shading indicates a Pareto efficient

strategy with a best (or tied for best) result among all Pareto efficient strategies along at least one outcome metric.

Anytown Atlanta Morris Anytown Atlanta Morris Anytown Atlanta Morris Anytown Atlanta MorrisCARE COORDINATION, HEALTHIER BEHAVIORS, AND ENVIRONMENTAL SAFETY

Coord (with assessment updating)

Coord + Behavior (CB)

Coord + Behavior + Environment (CBE)


CB + PreScreen

CB + PreScreen + Discharge

CBE + PreScreen + + +CBE + PreScreen + Discharge + + + + + + + + + + + +FURTHER FOCUS ON HEALTH

CB + SelfCare (CBS) +CBS + PrimaryCare

CBS + HospInfect + +CBE + SelfCare (CBES)

CBES + PrimaryCare

CBES + HospInfect + +FURTHER FOCUS ON EQUITY & PRODUCTIVITY

CBS + FamilyPath + + + + + + + + + + +CBS + MentalDisadv

CBES + MentalDisadv + +COMBINED APPROACHES USING SIX INTERVENTIONS

CBS + PrimaryCare + PreScreen + Discharge + +CBS + HospInfect + PreScreen + Discharge + + + + +CBS + MentalDisadv + PreScreen + Discharge + + + + + + + + +CBES + PrimaryCare + PreScreen + + + + + +CBES + HospInfect + PreScreen + + + + + + + + + +CBES + MentalDisadv + PreScreen + + + + + + + + +CBES + PreScreen + Discharge + + + + + + + + + + + +

Scenario 2 (CGP1, ACA0) Scenario 3 (CGP0, ACA1) Scenario 4 (CGP1, ACA1)Scenario 1 (CGP0, ACA0)

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