Lifetime Medical Expenditures Associated
with
ith Physical
Ph i l Activity
A ti it and
dO
Overweight
i ht –
A Simulation Approach
Jeroen van Meijgaard – UCLA School of Public Health
Academy Health
June 2010
BACKGROUND
 Physical Activity and Obesity are predictors for morbidity,
mortality
t lit and
d medical
di l expenditures
dit
 Physical Activity and Obesity change over individual life
titimes
 Planning is difficult because no good long term follow up
data evaluating expenditure conditional on early life
physical activity and obesity
Simulation can be used: stochastic process for physical
activity and obesity combined w/ data on short term response
p
conditional on p
physical
y
activity
y and obesity
y
of expenditures
BACKGROUND -
continued
 Some analyses that estimate lifetime expenditures
conditional on obesity (but not physical activity) in early
life and mid-life:
mid life:
• Daviglus et al (2004): Observed data (Medicare linked
with Chicago Heart Study data)
– Steep increase in lifetime expenditures w/ obesity
– No data on survival
• Finkelstein et al (2008): Estimated expenditures
taking into account survival
– Small increase in expenditures w/ obesity –
increase is p
partially
y mitigated
g
by
y lower survival
• van Baal et al (2008): Markov model based on
Netherlands data using chronic disease states
– Decrease in expenditures w/ obesity due to
substantially lower survival
DATA – UNITED STATES, 2004
Parameter estimates and population level summary data to
calibrate the model,
model chosen to represent US population (2004):
1 Population by gender and age (NCHS - 2004)
1.
2. Physical Activity distribution by gender and age (NHIS – 2002-2006)
3 Body Mass Index distribution by gender and age (NHIS – 2002
3.
2002-2006)
2006)
4. Medical expenditures conditional on physical activity and body mass
index, by gender and age (Linked NHIS-MEPS – 2002-2006)
5. Mortality risks by gender and age (NCHS Vital Statistics – 2003-2005)
6. Relative risks of physical activity and body mass index on mortality
(NHIS-HPDP, 1990-1991, linked with mortality data, augmented with
estimates from the literature)
METHODS – SIMULATION AND SENSITIVITY ANALYSIS
 Data and processes are integrated into a
microsimulation framework, simulating life course of
individuals from birth till death
 Simulate processes of physical activity and body mass
index over the life course by age and gender
 Mortality and expenditures are conditional on physical
activity and body mass index by age and gender
 Uncertainty
U
t i t in
i iinputt parameters
t
is
i iincorporated
t d th
through
h
standard errors from estimates
 Simulated runs are repeated 1
1,000
000 times
times, each time for a
different parameter vector, randomly drawn from a
hypercube
METHODS – LIFE COURSE DATA PROCESSES
 Leisure Time Physical Activity is based on self reported
frequency and duration of moderate and vigorous
exercise, translated to METhrs/wk. Based on longitudinal
data process is modeled with two parts: annual variation
+ individual component that follows autoregressive
process:  it   it 1  it ,
ln( PAit )   s( PA )  Agei    it   it ,
 Body Mass Index is based on self reported weight and
heights. It is modeled as an autoregressive process,
where deviation of the population mean in BMI is based
in the deviation from the population mean in the previous
year plus a small innovation
 it   it 1   it ,
1
 LBMI it   x( LBMI )  Ageit , ln( PAit )    it
BMI it
METHODS – PARAMETERIZATION OF RR(PA)
1
Releattive Risk (RR
R)
0.8
RR 
1
1  0.0245  PA
0.6
04
0.4
0.2
0
0
8
16
24
32
40
48
Ph i l Activity
Physical
A ti it (PA in
i METhrs/w
METh / eek)
k)
56
64
METHODS – PARAMETERIZATION OF RR(BMI)
Parameterized Relative Risk of BMI on Mortality (Male by Age)
3.5
Relative Risk of Mortality
3
2.5
25
2
40
55
1.5
70
1
0.5
0
18
20
22
24
26
28
30
32
34
36
38
40
BMI
Parameterized Relative Risk of BMI on Mortality (Female by Age)
3.5
Relative Risk of Mortality
3
2.5
25
2
40
55
1.5
70
1
05
0.5
0
18
20
22
24
26
28
30
BMI
32
34
36
38
40
METHODS – IMPACT OF PA AND BMI ON EXPENDITURES
 Medical expenditures were estimated using linked NHISMEPS data from 2002-2006 using a GLM framework.
Subsequently expenditures were simulated conditional
Subsequently,
on PA and BMI stratified by gender (s) and age (a):


E ( MedExp)  g ( s, a, PA, BMI )   sa exp   i( sa ) Di 
 i

RESULTS – TOTAL MEDICAL EXPENDIUTRES
CONDITIONAL ON PA AND BMI AT AGE 25
C di i l on Status
Conditional
S
at Age=25
A
25
M l
Male
F
Female
l
LE
Avg Annual
Exp.
Cumulative
Exp.
LE
Avg Annual
Exp.
Cumulative Exp.
52.4
(ref)
4,862
(ref)
254,710
(ref)
57.0
(ref)
5,839
(ref)
332,784
(ref)
Overweight (25-29.9 kg/m2)
-0.1
(0.28)
+182
(0.04)
+9,206
(0.05)
-0.0
(0.32)
+179
(0.02)
+9,981
(0.02)
Class I Obesity (30-34.9 kg/m2)
-0.7
(<0.01)
+449
(<0.01)
+20,012
(0.01)
-0.3
(0.03)
+419
(<0.01)
+22,221
(<0.01)
Class II/III Obesity (>35 kg/m2)
-1.7
(<0.01)
+571
(<0.01)
+20,939
(0.03)
-0.8
(<0.01)
+705
(<0.01)
+35,202
(<0.01)
51.6
( f)
(ref)
5,040
( f)
(ref)
260,228
( f)
(ref)
56.5
( f)
(ref)
6,039
( f)
(ref)
341,201
( f)
(ref)
Somewhat Active (8-16 METhrs/wk)
+0.5
(<0.01)
-4
(0.40)
+2,191
(0.03)
+0.4
(<0.01)
-31
(0.23)
+324
(0.37)
Very Active (≥16 METhrs/wk)
+1.0
(<0.01)
-14
(0.34)
+4,091
(0.05)
+0.7
(<0.01)
-63
(0.21)
-106
(0.48)
BMI category
Normal weight (18.5-24.9 kg/m2) reference
Physical Activity category
Sedentary
y ((0-8 METhrs/wk)) – reference
RESULTS – ANNUAL EXPENDITURES BY AGE
CONDITIONAL ON BMI STATUS AT AGE 25
Average annual cost conditional on BMI
at age 25, males
Average annual cost conditional on BMI
at age 25, females
$10,212
$10,040
85+
$10,031
$10,058
$9,857
85+
$9,865
$10,612
$10,233
$9,893
$9,518
65 84
65-84
$9,150
$9,827
,
$9,382
65 84
65-84
$9,083
$9,252
$9,017
$5 663
$5,663
45-64
$5,659
$4,698
$5,357
$2,103
25-44
$3,905
$2,064
$3,650
25-44
$1,845
$1 648
$1,648
$0
18.5 - 24.9
$5,906
45-64
$5,139
$3,404
$3 175
$3,175
$5,000
$10,000
$0
$5,000
30.0 - 34.9
25.0 - 29.9
$6 172
$6,172
$5,496
35.0 +
$10,000
DISCUSSION
 Results similar to previous studies where physical
activityy was not incorporated:
p
impact
p
of BMI on lifetime
medical expenditures smaller than in observational study
(Daviglus et al, 2004); but positive where it is negative
on similar simulation study (van Baal et al, 2008).
 Differences in lifetime medical expenditures also driven
b change
by
h
iin lif
life expectancy
t
 Benefit of improvements in life expectancy may be
valued to compare total benefits
 Lack of data make it difficult to validate the model
 Assumed that long term impact of behavior is mediated
through behavior; however chronic disease states may
play important role