Economic
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Economic Effect
SG-2 Technical Brief
Assumptions 1. Studies show a 25% reduction in utilization (inpatient) with an increase in unemployment. Effect is indirect via loss of employment determined health insurance benefits. 2. The effect is probably "across-the-board" – e.g. even non-discretionary utilization is affected. The only exception is probably acute, trauma-related care. 3. By corollary, does utilization increase by 25% if unemployment declines? Probably by not as much.
Approach: 1. Get change in unemployment forecasts from Bureau of Labor Statistics and SG-2 analysis. Need. This is δ. Increasing unemployment is a positive number; while decreasing unemployment is a negative number. 2. The "utilization depressor" is µ. Usually set to -0.25 3. The "negative bias factor" is κ. Default is 25. Using this in an exponential accentuates the change on the positive (or increasing unemployment) side.
∆ = δ • µ • e (κ • δ ) Current Defaults:
The Impact of Change™
δ: Our current national set looks as follows:
Eastern Mass, SSM/Cent. Missouri and ANOVA were all run with these national numbers. Unemployment Curve Chart
"Change in unemployment" Impact
.02
.015
.01
.005
.
-.005
-.01 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
The Impact of Change™
∆: Applying the above equation (with defaults for κ and µ) to the national δ's, we get the following results:
∆
δ
Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
No kappa .0042 .0167 -.002 -.004 -.002 0 0 -.004 -.001 -.001
(with Kappa
-0.00105 -0.00417 0.0005 0.001 0.0005 0 0 0.001 0.00025 0.00025
-0.00117 -0.00634 0.000476 0.000905 0.000476 0 0 0.000905 0.000244 0.000244
"Delta" Chart ∆ 0.02
Pure
0.015
Kappa No Kappa 0.01
0.005
0
-0.005
-0.01 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
These ∆ 's are the "changes in rate" that will be applied to the utilization volumes (non-Medicare component – see below).
The Impact of Change™
Implementation: These "rates" are then integrated and applied to the non-Medicare component (for DRGs) or total (for OPCs) utilization volumes. Currently the Medicare fractions are "hard-coded" (non time & space dependent) into the core DRG table. Example of DRG Medicare fractions: DRG
DRG Description
Medicare Fraction
1.00 Craniotomy >17 X Trauma
0.3429158
1.10 Craniotomy >17 X Trauma
0.3429158
2.00 Craniotomy For Trauma >17
0.5328467
2.10 Craniotomy For Trauma >17
0.5328467
3.00 Craniotomy Age 0-17
0
3.10 Craniotomy Age 0-17
0
4.00 Spinal Proc
0.1917476
4.10 Spinal Proc
0.1917476
The rates can be applied to: 1. initial utilization volumes or 2. "nonlinearly" multiplied against population-driven utilization.
Treatment of Medicare Fractions There are two sources for medicare fraction: (1) "Hard-coded"in the DRG tables directly or (2) derived directly from the age-group identifier of the relevant dataset. E.g. If the age group is greater than 65 then the medicare fraction is 1; other wise it is zero. If the age grouping spans 65 then the medicare fraction is some number between 0 and 1 – appropriately weighted at the time that the age group designation is defined. The default is to use option (2); if option (2) does not yield a medicare fraction (e.g. for undefined or global age group designations) then option (1) will be applied. Option (2) also allows for the medicare fraction to change over time as the proportion of the population over 65 likewise changes.
The Impact of Change™
Results: National data; linear run. Using κ, m defaults Economic Population
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
157,348
157,170
156,203
156,244
156,383
156,460
156,462
156,462
156,599
156,640
156,679
157,348
161,549
166,054
170,891
176,088
181,679
187,701
194,194
201,204
208,782
216,987
-0.4% 37.9%
Population / Economic "Wedge" NHDS National 2000 - 2010 SG-2 Forecast Economic Population
250 200 150 100 50
0 2000 2001 2002 2003 2004 2005 2006 2007
2008
2009
2010
Economic Effect Detail 157,600 157,400 157,200 157,000 156,800 156,600 156,400 156,200 156,000 155,800 155,600 2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
The Impact of Change™
The Impact of Change™
SG-2 Technical Brief
Assumptions 1. Studies show a 25% reduction in utilization (inpatient) with an increase in unemployment. Effect is indirect via loss of employment determined health insurance benefits. 2. The effect is probably "across-the-board" – e.g. even non-discretionary utilization is affected. The only exception is probably acute, trauma-related care. 3. By corollary, does utilization increase by 25% if unemployment declines? Probably by not as much.
Approach: 1. Get change in unemployment forecasts from Bureau of Labor Statistics and SG-2 analysis. Need. This is δ. Increasing unemployment is a positive number; while decreasing unemployment is a negative number. 2. The "utilization depressor" is µ. Usually set to -0.25 3. The "negative bias factor" is κ. Default is 25. Using this in an exponential accentuates the change on the positive (or increasing unemployment) side.
∆ = δ • µ • e (κ • δ ) Current Defaults:
The Impact of Change™
δ: Our current national set looks as follows:
Eastern Mass, SSM/Cent. Missouri and ANOVA were all run with these national numbers. Unemployment Curve Chart
"Change in unemployment" Impact
.02
.015
.01
.005
.
-.005
-.01 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
The Impact of Change™
∆: Applying the above equation (with defaults for κ and µ) to the national δ's, we get the following results:
∆
δ
Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
No kappa .0042 .0167 -.002 -.004 -.002 0 0 -.004 -.001 -.001
(with Kappa
-0.00105 -0.00417 0.0005 0.001 0.0005 0 0 0.001 0.00025 0.00025
-0.00117 -0.00634 0.000476 0.000905 0.000476 0 0 0.000905 0.000244 0.000244
"Delta" Chart ∆ 0.02
Pure
0.015
Kappa No Kappa 0.01
0.005
0
-0.005
-0.01 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Year
These ∆ 's are the "changes in rate" that will be applied to the utilization volumes (non-Medicare component – see below).
The Impact of Change™
Implementation: These "rates" are then integrated and applied to the non-Medicare component (for DRGs) or total (for OPCs) utilization volumes. Currently the Medicare fractions are "hard-coded" (non time & space dependent) into the core DRG table. Example of DRG Medicare fractions: DRG
DRG Description
Medicare Fraction
1.00 Craniotomy >17 X Trauma
0.3429158
1.10 Craniotomy >17 X Trauma
0.3429158
2.00 Craniotomy For Trauma >17
0.5328467
2.10 Craniotomy For Trauma >17
0.5328467
3.00 Craniotomy Age 0-17
0
3.10 Craniotomy Age 0-17
0
4.00 Spinal Proc
0.1917476
4.10 Spinal Proc
0.1917476
The rates can be applied to: 1. initial utilization volumes or 2. "nonlinearly" multiplied against population-driven utilization.
Treatment of Medicare Fractions There are two sources for medicare fraction: (1) "Hard-coded"in the DRG tables directly or (2) derived directly from the age-group identifier of the relevant dataset. E.g. If the age group is greater than 65 then the medicare fraction is 1; other wise it is zero. If the age grouping spans 65 then the medicare fraction is some number between 0 and 1 – appropriately weighted at the time that the age group designation is defined. The default is to use option (2); if option (2) does not yield a medicare fraction (e.g. for undefined or global age group designations) then option (1) will be applied. Option (2) also allows for the medicare fraction to change over time as the proportion of the population over 65 likewise changes.
The Impact of Change™
Results: National data; linear run. Using κ, m defaults Economic Population
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
157,348
157,170
156,203
156,244
156,383
156,460
156,462
156,462
156,599
156,640
156,679
157,348
161,549
166,054
170,891
176,088
181,679
187,701
194,194
201,204
208,782
216,987
-0.4% 37.9%
Population / Economic "Wedge" NHDS National 2000 - 2010 SG-2 Forecast Economic Population
250 200 150 100 50
0 2000 2001 2002 2003 2004 2005 2006 2007
2008
2009
2010
Economic Effect Detail 157,600 157,400 157,200 157,000 156,800 156,600 156,400 156,200 156,000 155,800 155,600 2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
The Impact of Change™
The Impact of Change™
