Milliman Research Report
Prepared by:
Jonathan Shreve, FSA, MAAA
Mary van der Heijde, FSA, MAAA
Peer reviewed by:
Tom Attaway, FSA, MAAA
April 2009
Impact of height,
weight, and smoking on
medical claim costs
Research from the annual update
of Milliman’s Medical Underwriting Guidelines
Milliman, whose corporate offices are in
Seattle, serves the full spectrum of
business, financial, government, and
union organizations. Founded in 1947 as
Milliman & Robertson, the company has
49 offices in principal cities in the United
States and worldwide. Milliman employs
more than 2,100 people, including a
professional staff of more than 1,100
qualified consultants and actuaries. The
firm has consulting practices in employee
benefits, healthcare, life insurance/
financial services, and property and
casualty insurance. Milliman’s employee
benefits practice is a member of Abelica
Global, an international organization of
independent consulting firms serving
clients around the globe. For further
information visit www.milliman.com.
Milliman
Research Report
Table of Contents
EXECUTIVE SUMMARY
2
MEDICAL EXPENDITURE PANEL SURVEY (MEPS)
3
CREATING AN UNDERWRITING SCENARIO USING MEPS
5
Relative predictive value of medical, build, and tobacco use information
5
Ratios of actual/expected costs by height/BMI before condition and tobacco points
5
Variation of residuals by height/BMI after accounting for medical history
7
Does smoking status add value in the underwriting process?
9
Effect of medical underreporting on underwriting
10
BODY MASS INDEX (BMI) AND ROHRER’S INDEX
12
Definition of BMI and Rohrer’s Index
12
Use and criticism of BMI
13
Distribution of BMI
15
Rohrer's Index
16
Improving linear correlation of BMI with costs and morbidity
19
ACKNOWLEDGEMENT
Caveats and limitations
20
20
Milliman
Research Report
EXECUTIVE SUMMARY
Every 12 to 18 months, Milliman updates the information in its Medical Underwriting Guidelines™
(MUGs). As part of our most recent update, we performed a special study of the impact of build (height
and weight) and smoking status on medical costs. For this update, we used a government data source,
the Medical Expenditure Panel Surveys (MEPS). The research led to a number of interesting conclusions,
which we describe in this report. Here are the highlights:
ƒƒ All elements of medical underwriting, including medical history, build, and smoking status, add
predictive power to an underwriter’s rates. Interestingly, while medical history adds the greatest power,
build information alone can yield about half of it. Smoking turns out to be the least important:
−− R2 (the proportion of variability in a data set that is accounted for the statistical model) using age and
gender information only = 13%
−− R2 using age, gender, and build information = 20%
−− R2 using age, gender, and medical-history information = 27%
−− R2 using age, gender, medical-history, build, and smoking-status information = 30%
ƒƒ Controlling for age and gender differences, we found that smokers cost about 9% more than
nonsmokers. However, if an underwriter has information available about an applicant’s other medical
conditions, there is about a 5% difference in unexplained costs between smokers and nonsmokers.
ƒƒ We found that the body mass index (BMI) is not the most accurate way to predict additional healthcare
costs. Roughly speaking, the BMI uses weight divided by height squared, and we found that weight
divided by height cubed is more accurately correlated with the heights and weights to which the
MUGs assigns no points or additional debit points (indicating greater medical cost). We also show
that this calculation provides us better correlation with the overall healthcare expenditure of a person
as well as with the incidence of health conditions influenced by physical build.
ƒƒ We assume that medical conditions are usually underreported to underwriters. We studied the effect
of underreporting medical conditions on the underwriting process. We concluded that part of the
reason to assign build debit points is to compensate for the underreporting of medical conditions. We
assumed that only 70% of medical costs are actually reported on medical applications.
The remainder of this report investigates these connections in more detail.
Note: Milliman, Inc. produces the Small Group Medical Underwriting Guidelines™ and the Individual
Medical Underwriting Guidelines™. Both of these products are referred to as the Medical Underwriting
Guidelines (MUGs) throughout this report.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
2
Milliman
Research Report
MEDICAL EXPENDITURE PANEL SURVEY (MEPS)
We used the household component of the Medical Expenditure Panel Survey (MEPS) data in our
analysis. MEPS is a database of survey responses collected from families, individuals, medical providers,
and employers. It is compiled by the Agency for Healthcare Research and Quality. The data collected is
representative of the United States demographically, health-wise, and with regard to several other factors,
including income and coverage levels.1
We chose MEPS for our research because it is one of the few available data resources that includes
height and weight (build) plus smoking-status information in the demographics. It also includes selfreported medical information, much like the information available to medical underwriters during
their deliberations. Therefore, this database represents a valuable resource from which we can draw
preliminary empirical conclusions about the role of a person’s build and smoking status and evaluate the
usefulness of this information in an underwriting application.
We chose MEPS for our
research because it is one
of the few available data
resources that includes
height and weight (build) plus
smoking-status information in
the demographics.
We sampled data from surveys of 1996 through 2004. We used the following elements from MEPS data:
ƒƒ age
ƒƒ gender
ƒƒ height
ƒƒ weight
ƒƒ smoking status
ƒƒ total healthcare expenditure
ƒƒ self-reported medical conditions, reported as three-digit ICD-9 codes
The characteristics of MEPS data summarized by gender and age categories are shown in the table in
Figure 1. This MEPS sample population has an age-gender distribution that is older than that expected
from a typical commercial population, based upon Milliman’s Health Cost Guidelines—Commercial
Rating Structures. We excluded children (age 18 and below) from this analysis. Figure 1 shows a highlevel comparison of the data used in our study.
Figure 1: Demographic Distribution in MEPS Data
Age
MEPS % Commercial Insurance %
Male
19-44
22,442
23%
29%
45-65
15,839
16%
18%
65+
7,101
7%
1%
24,812
26%
30%
21%
Female
19-44
45-65
17,643
18%
65+
10,033
10%
1%
97,870
100%
100%
Total
1
MEPS Lives
Agency for Healthcare Research and Quality. Medical Expenditure Panel Survey (2007). Retrieved June 15, 2008, from
http://www.meps.ahrq.gov/mepsweb/survey_comp/household.jsp.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
3
Milliman
Research Report
The table in Figure 2 shows the adult total healthcare expenditure per-member per-year (PMPY) for care
provided during the year (normalized to 2004 dollars). This includes out-of-pocket payments, payments
by private insurance, and other sources.
Figure 2: PMPY Costs by 5-Year Age/Gender Bands (adults only)
GenderAge
Total MEPS Population
MEPS PMPY Claim Cost
Adult Male
19-25
1,860
$786 25-29
4,828
$906 30-34
5,097
$1,319 35-39
5,223
$1,529 40-44
5,434
$2,154 $2,758 45-49
5,087
50-54
4,441
$3,578 55-59
3,607
$4,929 60-64
2,704
$5,719 65+
7,101
$7,890 45,382
$3,337 $1,933 Composite/Total
Adult Female
19-25
2,215
25-29
5,266
$2,416 30-34
5,533
$2,668 $2,909 35-39
5,756
40-44
6,042
$2,997 45-49
5,567
$3,491 50-54
4,988
$4,347 55-59
3,934
$4,980 60-64
3,154
$5,525 65+
10,033
$8,033 52,488
$4,293 97,870
$3,850 Composite/Total
All Adult Members
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
4
Milliman
Research Report
CREATING AN UNDERWRITING SCENARIO USING MEPS
In a typical underwriting process, an underwriter gathers information about an applicant’s medical
conditions, build, and tobacco use. Debit points may be assigned for each of these items. Typically, the
underwriter considers somewhat detailed information in assigning the medical points, and relies upon a
table to assign the build and tobacco-use points. This description is consistent with Milliman’s Medical
Underwriting Guidelines (MUGs). Thus, in assigning debit points for build or tobacco, we are interested
in what portion of the medical cost is not predicted by demographics or medical conditions. Statistically,
we are interested in how well build or tobacco use predicts the residual expenditure.
We created an underwriting scenario to measure the effectiveness of build and tobacco use in explaining
the variability in future healthcare costs. We used condition ratings from the MUGs to determine the next
year’s healthcare costs for each individual in the study, based on their self-reported medical-condition
information from the year before. We used reported height and weight, plus an indicator for smoking, to
further explain any residual costs after considering medical conditions.
We created an underwriting
scenario to measure the
effectiveness of build and
tobacco use in explaining
the variability in future
healthcare costs.
MEPS data from 1996 to 2004 was used in the study. The information for each person includes two
years of observed data. The survey is designed as overlapping panels in which half the population in
any year is reporting their second year of data, and about half is reporting their first. The data includes
self-reported healthcare conditions as well as insurance expenditures in each year. Thus, we use the selfreported conditions from the first year to predict costs for the second year. All expenditures were trended
to 2004 levels to normalize medical-expenditure trend effects.
The following section investigates the underwriting value added by various MUGs components such as
age, gender, medical history, build, and smoking status.
Relative predictive value of medical, build, and tobacco-use information
We started our study by calculating mean costs by five-year age and gender bands, incrementally adding
factors of medical history, build, and smoking status to the model. We then measured the effectiveness of
the model at each step. R2 was used as the measure of effectiveness of the model. R2 is a good yardstick
of model effectiveness because it measures the portion of the data variance explained by a predictive
model. It is also commonly used and well understood in the context of predictive modeling, of which
underwriting is an application.
For each person in the study, we made four predictions of costs per adult per year (PAPY). The first was
based on age and gender alone, the second added build information, the third included medical history
but not build or tobacco use, and the fourth added build and smoking status to the third. Each predicted
PAPY produced an increasing R2:
We started our study by
calculating mean costs by
five-year age and gender
bands, incrementally adding
factors of medical history,
build, and smoking status to
the model. We then measured
the effectiveness of the model
at each step.
ƒƒ R2 using age and gender information only = 13%
ƒƒ R2 using age, gender, and build information (no medical history) = 20%
ƒƒ R2 using age, gender, and medical-history information = 27%
ƒƒ R2 using age, gender, medical-history, build, and smoking-status information = 30%
A higher R2 indicates better prediction of actual costs by the model. We see that R2 increases
incrementally with each additional variable used to calculate the predicted cost. The largest increase
occurs with medical conditions, but we also get an additional increase with build and tobacco use.
Next, we looked at how well the MUGs predict costs for various BMI ranges and heights. The yardstick
used to measure predictive accuracy was residual cost that remained unexplained after accounting for
variables such as age, gender, and medical history.
Ratios of actual to expected costs by height/BMI before condition and tobacco points
The tables in Figures 3-6 summarize the ratio of actual to expected healthcare costs by height and
BMI, with no adjustment for information about the patient’s medical history or smoking status. The
expected costs are based only on the age-gender information about the individuals. A ratio of under
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
5
Milliman
Research Report
100% indicates that actual costs for these individuals are lower than the expected levels. Ratios of
over 100% indicate that costs are higher than expected. In any underwriting application, we expect to
see a distribution of ratios over and under 100%. This results from the fact that future costs cannot be
predicted perfectly. If the residuals are perfectly random, we have exhausted the predictive capability of a
particular model, i.e., we have done the best job that is possible in fitting that model. However, if we see a
systemic pattern in the residuals, then that indicates room for further modeling.
The table in Figure 3 shows this information for the male population.
Figure 3: Ratio of Actual Healthcare Costs to Age-Gender Expected Costs
(before accounting for medical history) for Males by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
16.5-18.5
18.5-25
25-30
30-35
35-40
40-100
5'3'-5'5''
114.0%
81.8%
78.7%
81.7%
84.6%
100.8%
120.9%
132.8%
5'6'-5'8''
107.6%
95.7%
92.1%
95.8%
98.5%
117.1%
5'9'-5'11''
143.5%
99.5%
97.5%
100.2%
103.8%
117.4%
135.0%
6'0'-6'2''
159.2%
104.2%
98.8%
102.2%
105.8%
133.6%
180.2%
The table in Figure 3 identifies areas where the costs are greater than the age-gender estimates (orange)
and areas where the costs are lower (blue). We observe that actual costs are greater for severely
underweight and severely overweight people. For any particular column, if BMI is a strong predictor, then
the ratios should be fairly constant across various heights. This is clearly not true.
For data credibility purposes, the sample size is shown in Figure 4.
Figure 4: Sample Size for Male Population by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
16.5-18.5
18.5-25
25-30
30-35
35-40
5'3'-5'5''
134
4,116
9,691
11,901
8,483
3,135
40-100
839
5'6'-5'8''
365
12,014
26,577
32,730
22,896
9,087
2,611
5'9'-5'11''
463
16,663
34,137
42,197
28,317
11,547
3,112
6'0'-6'2''
385
10,999
22,516
27,659
18,602
7,563
2,097
We present similar tables for females in Figure 5.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
6
Milliman
Research Report
Figure 5: Ratio of Actual Healthcare Costs to Age-Gender Expected Costs
(before accounting for medical history) for Females by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
16.5-18.5
18.5-25
25-30
5'0'-5'2''
122.8%
96.5%
90.1%
92.6%
30-35
95.5%
114.5%
35-40
40-100
129.6%
5'3'-5'5''
107.9%
87.5%
90.4%
93.9%
104.4%
122.7%
140.4%
5'6'-5'8''
106.7%
96.8%
97.2%
100.9%
108.5%
125.4%
132.1%
5'9'-5'11''
103.9%
104.2%
103.6%
106.9%
111.7%
132.2%
147.2%
The patterns for females are similar to those for males, although perhaps less extreme.
The sample sizes for females are shown in Figure 6.
Figure 6: Sample Sizes for Females by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
16.5-18.5
18.5-25
25-30
30-35
35-40
40-100
5'0'-5'2''
841
13,900
27,260
32,825
22,970
10,722
4,543
5'3'-5'5''
1,534
27,549
45,824
53,731
31,120
15,195
6,056
5'6'-5'8''
1,797
18,363
28,062
31,860
17,722
9,403
4,080
487
4,441
6,294
7,257
3,878
2,251
901
5'9'-5'11''
One can draw two key conclusions from these tables:
1. Build clearly has an impact on expected medical costs. In fact, using age-gender and build raises the
R2 to 20% (compared to 13% without build).
2. Cost variation does not align directly with BMI. Stated another way, BMI may oversimplify the
relationships that exist between height and weight (and gender).
Variation of residuals by height and BMI after accounting for medical history
During medical underwriting, an underwriter assigns debit points for build in addition to medical history.
In this case, the underwriter is only looking for additional morbidity which may be explained by build and
not by reported medical conditions. For this part of our study, we first assigned debit points from the
MUGs for the self-reported medical conditions in MEPS. Then we looked at the ratio of actual claims to
expected claims based on the height and BMI of the individual.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
BMI may oversimplify the
relationships that exist
between height and weight
(and gender).
7
Milliman
Research Report
The tables in Figures 7 and 8 present the actual-to-expected ratios by height and BMI for males
and females separately. The expected costs here are based on the individual’s age, gender, and
medical history.
Figure 7: Ratio of Actual Healthcare Costs to Age-Gender Expected Costs
(after accounting for medical history) for Males by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
5'3'-5'5''
111.1%
16.5-18.5
85.3%
18.5-25
82.3%
25-30
84.5%
30-35
86.3%
35-40
97.4%
40-100
110.9%
5'6'-5'8''
99.9%
95.9%
93.9%
96.7%
98.6%
110.7%
119.6%
5'9'-5'11''
128.0%
101.0%
99.5%
101.3%
103.2%
110.4%
117.8%
6'0'-6'2''
148.0%
106.1%
100.6%
103.2%
105.0%
125.3%
153.4%
In the table in Figure 7, we observe lower ratios for shorter men (similar to Figure 3), and lower ratios
for men with closer to normal weight, using the BMI scale. This observation is expected based on our
intuition regarding heights and weights that are not within the normal range. We also see that ratios have
significantly less variation than those when only age and gender are used to predict cost.
We present a similar table for females in Figure 8.
Figure 8: Ratio of Actual Healthcare Costs to Age-Gender Expected Costs
(after accounting for medical history) for Females by Height and BMI Range
HeightBMI Range
Severely
Under- Under-Over-
Clinically Morbidly
weightweightNormalweightObeseObeseObese
0-16.5
35-40
40-100
5'0'-5'2''
115.7%
16.5-18.5
99.5%
18.5-25
93.1%
25-30
94.5%
30-35
94.7%
106.4%
113.8%
5'3'-5'5''
109.1%
92.1%
94.0%
96.2%
102.8%
112.3%
120.4%
5'6'-5'8''
109.3%
100.5%
99.8%
102.0%
105.4%
114.3%
114.9%
5'9'-5'11''
111.0%
105.8%
105.5%
106.7%
108.9%
120.4%
130.5%
We see similar trends in females as were seen in males, including greater variation in costs with taller
individuals (although all categories of tall females show some degree of additional cost). The population
sample for males and females is the same as previously shown in Figures 4 and 6.
We also observed the
distribution of predictive
accuracy by build and saw
that the costs for extremely
overweight and extremely
underweight people are
consistently higher while costs
for others are lower.
We also observed the distribution of predictive accuracy by build and saw that the costs for extremely
overweight and extremely underweight people are consistently higher while costs for others are lower.
We used the results of this research (and judgment) to create the build tables that went into the 2008
MUGs. These tables also considered the effect of underreporting, which is discussed below. The table
in Figure 9 is a sample of the new build tables from the MUGs.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
8
Milliman
Research Report
Figure 9: Assigned Debit Points for Adult Males by Height and Weight
Height
5’8"
5’9"
5’10"
5’11"
6’0"
Average Weight
161
165
170
174
179
Assigned Points by Weight (lbs.)
<90
10
10
10
15
15
91-105
5
10
10
10
10
10
106-120
0
5
5
5
121-135
0
0
0
5
5
136-150
0
0
0
0
0
151-165
0
0
0
0
0
166-180
0
0
0
0
0
181-195
0
0
0
0
0
196-210
5
0
0
0
0
211-225
10
5
5
0
0
226-240
15
10
10
5
5
241-255
20
15
15
10
10
256-270
25
20
20
15
15
271-285
30
25
25
20
20
286-300
35
30
30
25
25
30
301-315
40
35
35
30
316-330
45
45
40
35
35
331-345
55
55
50
45
45
346-360
65
65
60
55
55
Does smoking status add value in the underwriting process?
Most underwriting applications request information about the smoking status of the applicant. Insurers
often use this information to differentiate rates for smokers and nonsmokers, charging a higher rate for
smokers in addition to any differentiation in rates indicated by the reported medical conditions. Although
it is accepted that smoking is bad for one’s health, it is possible that much of the increased cost for
members being underwritten is already captured from their detailed medical history. For example, if
smoking leads to emphysema, then there would already be extra costs assigned to the applicant for
emphysema specifically, so it could be double counting to also assign a smoking load.
Therefore, we investigated how including a smoking factor affects medical costs. The table in Figure 10
shows the variation of actual cost by age, gender, and smoking status, and the ratio of smoker to
nonsmoker costs.
Although it is accepted that
smoking is bad for one’s
health, it is possible that
much of the increased cost for
members being underwritten
is already captured from their
detailed medical history.
Figure 10: Variation of Actual Cost by Age, Gender, and Smoking Status
GenderAge
Male
Percent SmokerNonsmoker
Composition
by Gender
CountActual PMPY
CountActual PMPY
Ratio
<30
17.5%
1,495
$976
5,193
$843
116%
30-44
41.1%
3,803
$1,917
11,951
$1,600
120%
Male
45-65
41.4%
3,943
$3,957
11,896
$3,998
99%
Up to 65
100%
9,241
$2,635
29,040
$2,447
108%
Female
<30
17.6%
1,296
$2,288
6,185
$2,270
101%
30-44
40.8%
3,567
$3,084
13,764
$2,805
110%
Female
45-65
41.6%
3,394
$4,988
14,249
$4,296
116%
Up to 65
100%
8,257
$3,742
34,198
$3,329
112%
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
9
Milliman
Research Report
Male smokers cost on
average 8% more than their
nonsmoker counterparts,
and female smokers cost on
average 12% more than their
nonsmoker counterparts.
Male smokers cost on average 8% more than their nonsmoker counterparts, and female smokers cost on
average 12% more than their nonsmoker counterparts. However, this comparison ignores that smokers
are, on average, older than nonsmokers. If we adjust for the mix, male smokers are 6% more expensive
while the female comparison stays at 12% more expensive.
The table in Figure 11 shows the expected cost adjusted for age and gender, and the actual costs for
smokers and nonsmokers.
Figure 11: Expected Costs adjusted for Age and Gender, and Actual Costs
by Smoking Status—Male/Female up to Age 65
SmokerNonsmoker
Count
17,498
63,238
Expected PAPY Cost (adjusted for age and gender)
$2,925
$2,989
Actual PAPY Cost
$3,157
$2,924
$ 232
$ (65)
Unexplained Difference
For smokers, we see that there is an unexplained increase of $232 between actual and expected costs.
That figure is a $65 decrease for nonsmokers. Therefore, the net difference is $297, which represents
the additional cost that could be attributed to medical conditions. In total, this represents about a 9%
difference between smokers and nonsmokers.
With full information about
applicants’ medical history,
it is justified to increase
the smokers’ rates by an
additional 5%. If none of
the medical information is
available, an appropriate load
is 9%.
The table in Figure 12 shows the MEPS expected cost when a full medical history is taken into account.
In this case, the difference in costs is only about 5%. Put another way, with full information about
applicants’ medical history, it is justified to increase the smokers’ rates by an additional 5%. If none of the
medical information is available, an appropriate load is 9%.
Figure 12: Expected and Actual Costs Adjusted for Age, Gender, and Medical History,
by Smoking—Male/Female up to Age 65
SmokerNonsmoker
Count
17,498
63,238
Expected PAPY Cost (Adjusted for age, gender and medical history)
$3,044
$2,951
Actual PAPY Cost
$3,157
$2,924
Unexplained Difference
There seems to be a
correlation of costs with
extreme values in height/
weight combinations. These
correlations persist even after
we take medical conditions
into account.
$ 114
$ (26)
Effect of medical underreporting on underwriting
We can form some conclusions from our investigation into MEPS data using reported height, weight, and
medical conditions. First, there seems to be a correlation of costs with extreme values in height/weight
combinations. These correlations persist even after we take medical conditions into account. In other
words, height and weight—or information about the build of a person—offers underwriting assistance in
addition to medical underwriting. The Milliman MUGs were among the first tools to integrate this with the
medical underwriting component in an actuarially rigorous fashion.
Medical conditions, smoking status, and build all add predictive value. However, these are all selfreported on the medical insurance application. The logical follow-up question to this is: How much
can we rely on these self-reported variables? A common issue during medical underwriting is the
underreporting of information. Smokers know they will pay a higher rate for insurance if they report
their smoking status. Applicants know that the more medical conditions they have, the greater chance
they have of paying a higher premium, or worse, having coverage denied. Therefore, despite the ability
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
10
Milliman
Research Report
of insurers to rescind coverage upon discovering misrepresentation, a strong incentive remains for
insurance applicants to lie on medical applications.
We assumed that the MEPS expected cost provides a 100% reporting scenario of medical conditions
because there is no incentive for surveyed MEPS population to underreport. For the purposes of this
analysis, we considered only adults between the ages of 18 and 65, in order to better represent the
commercial population. To determine the effect of underreporting medical conditions on health insurance
applications, we calculated the expected costs based on age and gender only using the average PAPY
cost by age and gender categories. These age/gender-only costs represent a reporting scenario in
which no medical information is provided. The calculated expected cost using age/gender and medical
conditions represents a scenario in which we assume no underreporting. For purposes of this analysis,
we estimated that, in practice, only 70% of the costs of medical conditions are typically reported
to underwriters.
The MUGs are balanced with every update, to ensure that the expected debit points assigned reflect the
experience of carriers using the MUGs. Thus, the points effectively have underreporting already built into
them. That means an important question when actually constructing the build and smoking tables is how
to reflect this previous assumption.
If a company is at 100% underreporting (or 0% reporting), then the appropriate debit points to assign
for build and smoking would be based upon the tables above that do not take medical conditions
into account (Figures 3, 5, and 11). If a company has no underreporting, we would consider this to
be consistent with MEPS, so the correct debit points would be based upon fully taking the medical
conditions into account (Figures 7, 8, and 12).
At 30% underreporting, we therefore concluded that one should use the weighted average of these
tables. In effect, our smoking load should be 30% of the way from 5% extra cost (0% underreporting) to
9% extra cost (100% underreporting), or 6.2%. In the actual smoking table, we varied the load by age, so
there is not a direct correlation to this result.
The implications of underreporting to underwriters are important: If the methods used to gather medical
information are less complete, then more credibility should be assigned to build and smoking, and vice versa.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
The implications of
underreporting to underwriters
are important: If the methods
used to gather medical
information are less complete,
then more credibility should
be assigned to build and
smoking, and vice versa.
11
Milliman
Research Report
BODY MASS INDEX (BMI) AND ROHRER’S INDEX
Working with underwriters, we often hear the question, “Why don’t you use BMI for your build charts?”
The assumption of these underwriters, consistent with the popular literature today, is that BMI is the best
way to measure build. But compared to height and weight tables, BMI is actually more limiting, because
every combination of height and weight that produces the same BMI would have the same number of
debits assigned. We will start with a brief history of BMI in order to understand its popular use today.
In the 19th century, Adolphe Quetelet found that the weight distributions of French and Scottish army
inductees of similar statures followed a normal distribution. For each stature range, he defined the
average weight (which corresponds to the center of the distribution) as the “normal” weight. He also
observed that this average weight was proportional to height squared. The purpose of defining the
“normal” build was to identify social deviants and criminals based on the deviation of their build from that
of the “normal” man.2
In spite of these pseudo-scientific arguments of Quetelet, the use of BMI did not gain traction. Body
weight remained a contentious mechanism for social discrimination. Things looked promising for BMI
with the advent of insurance. In the early 20th century, the flourishing life-insurance industry was eagerly
looking for predictors of early mortality. A statistician in the Metropolitan Life Insurance Company, Louis
Dublin, charted death rates against heights and weights of the policyholders. Dublin found that those with
the "average" or "normal" build lived longer. He concluded without any clinical rationale that BMI was a
predictor of the likelihood of early death. Thus began the use of BMI as a popular yardstick of morbidity
and mortality that has largely resisted a more rigorous understanding.3
Based on our work, we have
concluded that another index
is a better measure of build
than BMI.
Based on our work, we have concluded that another index is a better measure of build than BMI.
Definition of BMI and Rohrer’s Index
The formula for computation of BMI is called a power law because it describes a linear relation between
weight and the square of height. BMI is calculated as follows.
In the metric system:
In the imperial system:
2
BMI = weight (in kg) / [height (in meters)]2
BMI = weight (in lbs) × 703 / [height (in inches)]2
Oliver, J. E. (2006). Fat Politics. New York: Oxford University Press.
Figure 13: BMI Reference Table by Height and Weight
Height
Weight
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
4' 0''
27
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
92
95
98
101
104
107
4' 2''
25
28
31
34
37
39
42
45
48
51
53
56
59
62
65
67
70
73
76
79
82
84
87
90
93
96
98
4' 4''
23
26
29
31
34
36
39
42
44
47
49
52
55
57
60
62
65
68
70
73
75
78
81
83
86
88
91
4' 6''
22
24
27
29
31
34
36
39
41
43
46
48
51
53
55
58
60
63
65
68
70
72
75
77
80
82
84
4' 8''
20
22
25
27
29
31
34
36
38
40
43
45
47
49
52
54
56
58
61
63
65
67
69
72
74
76
78
4' 10''
19
21
23
25
27
29
31
33
36
38
40
42
44
46
48
50
52
54
56
59
61
63
65
67
69
71
73
5' 0''
18
20
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
62
64
66
68
5' 2''
16
18
20
22
24
26
27
29
31
33
35
37
38
40
42
44
46
48
49
51
53
55
57
59
60
62
64
5' 4''
15
17
19
21
22
24
26
27
29
31
33
34
36
38
39
41
43
45
46
48
50
51
53
55
57
58
60
5' 6''
15
16
18
19
21
23
24
26
27
29
31
32
34
36
37
39
40
42
44
45
47
48
50
52
53
55
56
5' 8''
14
15
17
18
20
21
23
24
26
27
29
30
32
33
35
36
38
40
41
43
44
46
47
49
50
52
53
5' 10''
13
14
16
17
19
20
22
23
24
26
27
29
30
32
33
34
36
37
39
40
42
43
44
46
47
49
50
6' 0''
12
14
15
16
18
19
20
22
23
24
26
27
28
30
31
33
34
35
37
38
39
41
42
43
45
46
47
6' 2''
12
13
14
15
17
18
19
21
22
23
24
26
27
28
30
31
32
33
35
36
37
39
40
41
42
44
45
<18.5
Underweight
18.5-25
Normal weight
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
25-29.9
Overweight
30-39.9
Obese
>39.9
Morbidly Obese
12
Milliman
Research Report
BMI is the most common measure of weight and obesity. It is so common that if you look for published
articles that quantify the impact of build on medical cost or prevalence of particular conditions (which we
did), the researchers simply assume that the BMI definition of “underweight,” “normal weight,” etc., is a
given. We did not find any articles that consider whether relative height or weight might have an impact.
The following definitions are used for this purpose:
ƒƒ underweight: BMI < 18.5
ƒƒ normal weight: BMI in the range 18.5–24.9
ƒƒ overweight: BMI in the range 25.0–29.9
ƒƒ obese: BMI in the range 30.0–39.9
ƒƒ morbidly obese: BMI > 39.93
A major criticism of BMI as a measure of obesity is the arbitrariness by which someone is classified
as underweight, normal, overweight, or obese. According to an account in J. Eric Oliver’s book, Fat
Politics, the thresholds for these distinctions have moved back and forth over the last two decades. The
latest guidance comes from the World Health Organization, whose cutoffs were endorsed by a National
Institutes of Health (NIH) team of experts in 1988. The panel recommended thresholds that placed about
37 million Americans in the overweight class. This caused controversy, and the arguments that ensued
were political in nature rather than focused on the scientific arguments underlying the recommendations.
The scientific evidence, according to Oliver, was “in fact nonexistent”. According to the NIH report, the
recommendation of setting the overweight threshold at a BMI of 25 or higher was based on a less than
fully understood relation to mortality. The level of rigor applied by the panel can be gauged by the fact
that a report used as a basis for their recommendations was in fact inconsistent with the conclusions of
the panel. Nutritionist Richard Toiano had found that mortality was highest among the very thin as well as
the very heavy, and also that increased mortality was typically not evident until well beyond a BMI of 30.
Further, differences in mortality are statistically insignificant until well beyond a BMI of 40.
A major criticism of BMI
as a measure of obesity is
the arbitrariness by which
someone is classified
as underweight, normal,
overweight, or obese.
In the prior section, we used a combination of height and BMI to determine when medical costs vary
from their expected levels. And, as we saw in that section, height has a fairly significant impact on the
calculations (that is, the assumption that a 25 BMI will have the same effect on people of every height
does not hold true).
Use and criticism of BMI
BMI suffers from several limitations. Underwriters are not in a position to overcome some of these
limitations, without paramedical visits to each applicant. BMI measures the build of a person and not
body fat (adiposity). However, it is adiposity, and not build, that is linked to adverse health outcomes
such as diabetes,4 heart disease,5 and many other conditions. For this reason, BMI as a measure of
body fat does not apply to certain people, such as some professional athletes who may be identified as
overweight by BMI measures.6
BMI measures the build of
a person and not body fat
(adiposity). However, it is
adiposity, and not build,
that is linked to adverse
health outcomes.
The BMI formula is called a squared power law because of the relation between weight and height
squared. From our data, it appears that a modification to the power law—using cubed rather than
squared—would give better results.
3
4
5
6
World Health Organization Technical report series 894. "Obesity: preventing and managing the global epidemic." Geneva:
World Health Organization, 2000. PDF. ISBN 92-4-120894-5.
Meisinger, C.,A., B. Döring, M. Heier Thorand, and H. Löwel. "Body Fat Distribution and Risk of Type 2 Diabetes in the General
Population: Are There Differences between Men and Women? The MONICA/KORA Augsburg Cohort Study." American
Journal of Clinical Nutrition 84: 483-9. 9 Sep 2007. Available online at http://www.ajcn.org/cgi/content/abstract/84/3/483.
Seidell, Jacob C., Claude Bouchard, Kathryn M. Rexrode, Vincent J. Carey, Charles H. Hennekens, Ellen Walters, Graham A.
Colditz, Meir J. Stampfer, Walter C. Willett, and JoAnn E. Manson. Abdominal Adiposity and Risk of Heart Disease. JAMA.
1999;281(24):2284-2285.
MSNBC Health/Fitness. "Bye-bye BMI? Tape may measure obesity better." (2006). Retrieved Sept 1, 2008, from:
http://www.msnbc.msn.com/id/14483512/.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
13
Milliman
Research Report
BMI is a measure of a
person’s build and is affected
by body proportions. As a
result, a particular BMI is not
necessarily a good predictor
of a person’s healthcare costs.
BMI is a measure of a person’s build and is affected by body proportions. As a result, a particular BMI is
not necessarily a good predictor of a person’s healthcare costs. The table in Figure 14 uses the MEPS
data and show that the same BMI of 22 for females between 40 and 49 years old could have several
average costs associated with it. This suggests a lack of homogeneity in the use of BMI as an indicator of
healthcare costs.
Figure 14: Costs Variation by Height and Weight for the Same BMI (females, age 40-49)
Actual/Expected
Average
Cost with
Height
WeightBMI
Condition
Count
Cost
Cost
5'0''
106-120
22
126
$ 315,910
$ 2,507 76.9%
5'4''
121-135
22
336
$ 877,648
$ 2,612 90.9%
5'7''
136-150
22
218
$ 759,566
$ 3,484 114.3%
5'11''
151-165
22
17
$ 81,433
$ 4,790 117.2%
This lack of homogeneity is not just present at a healthier BMI of 22. A BMI of 27 among females exhibits
a similarly wide range of costs associated with it, shown in Figure 15.
Figure 15: Costs Variation by Height and Weight for the Same BMI (females, age 40-49)
Actual/Expected
Average
Cost with
Height
WeightBMI
Condition
Count
Cost
Cost
5'1''
136-150
27
171 $ 455,829 $ 2,666 82.2%
5'4''
151-165
27
243 $ 767,353 $ 3,158 101.9%
5'7''
166-180
27
150 $ 382,838 $ 2,552 85.7%
5'10''
181-195
27
14 $ 30,401 $ 2,172 66.8%
However, the actual-to-expected cost ratio is much more consistent if we use the Rohrer’s index (RI),
as shown in the tables in Figures 16 and 17. This consistency means that we may be able to better
associate a particular RI value with a reasonable range of costs, which is not possible with BMIs.
Figure 16: Costs Variation by Height and Weight for the Same Rohrer’s Index
(females, age 40-49)
Actual/Expected
Average
Cost with
Height
Weight
RI
Condition
Count
Cost
Cost
5'1''
106-120
1.4
117 $ 341,508 $ 2,919 5'4''
121-135
1.4
336 $ 877,648 $ 2,612 90.9%
5'5''
136-150
1.4
270 $ 818,617 $ 3,032 108.1%
5'8''
151-165
1.4
105 $ 252,732 $ 2,407 79.8%
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
96.9%
14
Milliman
Research Report
Again, this lack of homogeneity is not just present at a healthier RI of 1.4. An RI of 1.6 among females
exhibits a similarly wide range of costs associated with it, as shown in Figure 17.
Figure 17: Costs Variation by Height and Weight for the Same Rohrer’s Index
(females, age 40-49)
Actual/Expected
Average
Cost with
Height
Weight
RI
Count
Cost
Cost
Condition
5'0''
121-135
1.6
5'4''
136-150
1.6
182 $ 472,410 $ 2,596 83.5%
276 $ 599,634 $ 2,173 5'7''
151-165
75.1%
1.6
207 $ 587,890 $ 2,840 5'11''
166-180
82.2%
1.6
150 $ 382,838 $ 2,552 85.7%
With this background, we return to the question of whether BMI in its current form is the best indicator
in a health underwriting application of build. Health underwriting is concerned with two relevant and
correlated outcomes: future use of healthcare dollars and resources for each applicant. To study the
relationship between BMI and healthcare expenditure and utilization, we ran several tests on BMI using
MEPS data. We present these tests and their findings below.
Distribution of BMI
BMI calculation is based on the premise that it can distribute individuals in a way that the counts
would represent a bell curve, or a normal distribution. The distribution of BMI is important from a health
underwriting perspective. The difference between the future healthcare costs predicted by medical
underwriting and actual costs would have a certain distribution. If the normal distribution of BMI is
correlated with that distribution, then an appropriate selection of thresholds can complement medical
underwriting in the form of better predictions regarding future resource use.
Health underwriting is
concerned with two relevant
and correlated outcomes:
future use of healthcare
dollars and resources for
each applicant.
We studied the distribution of BMI from the MEPS data. The counts were adjusted from the MEPS data
to reflect a commercially insured population. Figure 18 shows the distribution of male BMI. We see that
it is roughly a bell-shaped curve, with a positive skewness (coefficient of skewness is 0.51), indicating
that the distribution is not exactly symmetric, but that there is slightly more probability to the right of the
expected value than to the left. The average BMI in the population is 27.30.
Figure 18: Distribution of Adult Male BMI in MEPS
Adult Male BMI Distribution in MEPS
7,000
Frequency
6,000
5,000
4,000
3,000
2,000
1,000
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
BMI
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
15
Milliman
Research Report
Figure 19 shows the same information for adult females. We see that the distribution is even more
positively skewed than the male BMI distribution (coefficient of skewness is 1.21). The average BMI in
the population is 26.95.
Figure 19: Distribution of Adult Female BMI in MEPS
Adult Female BMI Distribution in MEPS
7,000
Frequency
6,000
5,000
4,000
3,000
2,000
1,000
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
BMI
Rohrer’s Index
Anthropometry refers to the measurement of human physical features; the BMI formula is one instance of
this practice. The BMI formulation is one possible way in which to combine height and weight information
into a singular metric. Another formulation is called Rohrer’s index, which uses the cube of height in
the denominator:
Rohrer’s index = Body weight (in grams) ×100 / [Height (in centimeters)]3
We note the different units used to calculate the metric form of Rohrer’s versus BMI, which uses the units
of kilograms and meters.
Using imperial units, the formula is
Rohrer’s index = Body weight (in pounds) × 2,768 / [Height (in inches)]3
The purpose of an anthropometric index in an underwriting context or as a health-status determinant is
to assign each person a number based on his or her body proportions such that we can develop ranges
of these numbers where each range corresponds to a health status (or expected healthcare costs) using
easily available measurements (such as height and weight).
Using Rohrer’s index, we
approximate the human body
as a cube with each side equal
to the body’s height and find
the weight per unit volume.
Intuitively, one would start by taking ratios of weight to height and check the correlation between the
ratio and health status/healthcare cost. A simple ratio of weight (in kilograms) to height (in meters)
would tell us the average kilograms that one would find in a meter of human body measured along the
length of the body. This linear ratio crudely approximates the human body as a one-dimensional object
(such as a string) and assumes that one’s height does not affect the width or depth of one’s body. To
better approximate a human body, we could find the weight per unit area. Here, we would approximate
the human body as a square sheet with each side as large as the body’s height. BMI uses this planar
approximation. Rohrer’s index uses the cubic form, and it assumes that a person’s depth and girth are
proportionate to height.
Using Rohrer’s index, we approximate the human body as a cube with each side equal to the body’s
height and find the weight per unit volume. Rohrer’s index is much less used than BMI and its application
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
16
Milliman
Research Report
is predominantly seen as an anthropometric index for children. One reason is that this index may be less
correlated with age than the traditional form.7
All these indices assume uniform weight distribution. Intuitively, the linear form is assuming concentration of the
weight of the body along a very thin line, and the cube form assumes the body weight spread over a very large cube.
It turns out that Rohrer’s index is more consistent with the underwriting objective of stratifying health
risks. For example, the MUGs include build charts that allow underwriters to rate on height and weight
information after considering the risks from medical conditions. There is a normal or standard range
where no points are awarded. A desirable aspect of the BMI calculation from an underwriting perspective
is that it should produce consistent threshold values for the maximum and minimum points of this range.
A risk-stratification scheme, where risk band thresholds are a moving target, is difficult both to justify and
to use. With this background, consider the tables in Figures 20 and 21.
It turns out that Rohrer’s
index is more consistent with
the underwriting objective of
stratifying health risks.
The table in Figure 20 shows the traditional BMI calculation by height and weight, presented only for
the range where the MUGs suggest that no additional risk is expected given ratings for demographic
and medical conditions. We see that there is a wide range of possible BMI values at the maximum and
minimum thresholds of standard combinations of height and weight. Standard in this context implies that
this combination of height and weight presents no additional underwriting risk. For the maximum values,
the BMIs range from 25 to 32. For the minimum thresholds they range from 13 to 22.
Figure 20: Adult Male Build Chart—Body Mass Index (BMI) by Height and Weight
Height
Weight
< 90
91105
106120
121135
136150
5'0"
15
19
22
25
5'1"
14
19
21
24
27
5'2"
14
18
21
23
26
5'3"
13
151165
166180
181195
17
20
23
25
5'4"
17
19
22
25
27
5'5"
16
19
21
24
26
29
5'6"
18
21
23
25
28
5'7"
18
20
22
25
27
29
5'8"
17
196210
226240
241255
256270
271285
286300
28
19
22
24
26
29
5'9"
19
21
23
26
28
30
5'10"
18
21
23
25
27
29
5'11"
20
22
24
26
28
30
6'0"
19
21
23
25
28
30
6'1"
19
21
23
25
27
29
31
6'2"
20
22
24
26
28
30
6'3"
20
22
23
25
27
29
6'4"
21
23
25
27
28
30
6'5"
21
22
24
26
28
29
31
6'6"
22
23
25
27
29
30
6'7"
21
6'8"
23
25
26
28
30
31
22
24
26
27
29
31
Underweight
7
211225
Normal Weight
32
Overweight
Theoretical problems with indices using height and weight. Available online at
http://www.unsystem.org/scn/archives/adolescents/ch06.htm#Which%20anthropometric%20index.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
17
Milliman
Research Report
The table in Figure 21 presents the Rohrer’s index calculation. We see that the threshold values are
much more consistent for various heights. For example, the maximum BMI values in the standard range
in Figure 20 vary in the relatively narrow band of 25–31. The difference is more stark for the minimum
bound of this range, where the BMI varies from 13–22.
Figure 21: Adult Male Build Chart—Rohrer’s Index, Body Mass Index (BMI),
by Height and Weight
Height
Weight
< 90
91105
106120
121135
136150
151165
5'0"
1.2
1.3
1.4
1.6
5'1"
1.1
1.2
1.4
1.6
1.7
5'2"
1.0
1.1
1.3
1.5
1.7
5'3"
1.0
1.1
1.3
1.4
1.6
1.7
5'4"
1.0
1.2
1.4
1.5
1.7
5'5"
1.0
166180
181195
1.1
1.3
1.4
1.6
1.7
5'6"
1.1
1.2
1.4
1.5
1.7
5'7"
1.0
1.2
1.3
1.5
1.6
1.7
5'8"
1.0
196210
1.1
1.3
1.4
1.5
1.7
5'9"
1.1
1.2
1.3
1.5
1.6
1.7
5'10"
1.0
211225
1.2
1.3
1.4
1.5
1.6
5'11"
1.1
1.2
1.3
1.5
1.6
1.7
6'0"
1.1
1.2
1.3
1.4
1.5
1.6
6'1"
1.0
226240
1.1
1.2
1.3
1.4
1.6
1.7
6'2"
1.1
1.2
1.3
1.4
1.5
1.6
6'3"
1.0
1.1
1.2
1.3
1.4
1.5
241255
6'4"
1.1
1.2
1.3
1.4
1.5
1.6
6'5"
1.0
1.1
1.2
1.3
1.4
1.5
256270
271285
1.6
6'6"
1.1
1.2
1.3
1.4
1.4
1.5
6'7"
1.1
1.1
1.2
1.3
1.4
1.5
1.6
1.1
1.2
1.3
1.3
1.4
1.5
6'8"
Underweight
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
Normal Weight
286300
1.6
Overweight
18
Milliman
Research Report
On this evidence, it appears that Rohrer’s index as presented in this section offers more consistent
risk stratification from an underwriting perspective than does the traditional form. Figure 21 shows that
the maximum end of the standard range varies from 1.6-1.7, and the minimum end varies from 1.0-1.1.
Figures 22 and 23 present the new BMI distribution using Rohrer’s index instead of the traditional
squared form.
On this evidence, it appears
that Rohrer’s index as
presented in this section
offers more consistent
risk stratification from an
underwriting perspective than
does the traditional form.
Figure 22: Adult Male Rohrer'sAdult
Index
Distribution
MEPS
Male
Rohrer's Indexin
Distribution
in MEPS
8000
Frequency
7000
6000
5000
4000
3000
2000
1000
0
0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
2
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8
Rohrer's Index
For males, Rohrer’s shows a slightly more distinct normal distribution than the usual calculation.
Figure 23: Adult Female Rohrer's Index Distribution in MEPS
Adult Female Rohrer's Index Distribution in MEPS
8000
Frequency
7000
6000
5000
4000
3000
2000
1000
0
0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
2
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8
Rohrer's Index
For females, the two calculations result in similarly positively skewed distributions.
Improving linear correlation of BMI with costs and morbidity
The use of BMI in underwriting is predicated on the ability of the height and weight of an individual to
tell us something about future healthcare costs and utilization. We have seen in prior sections that the
build of a person represents significant additional information that helps to improve the underwriting of
health risks.
In the prior section we saw that the Rohrer’s index (RI) presents us with consistent threshold values for
risk stratification when used in conjunction with medical information. In this section, we test the overall
correlation of RI and BMI with healthcare costs and selected health conditions.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
The use of BMI in underwriting
is predicated on the ability of
the height and weight of an
individual to tell us something
about future healthcare costs
and utilization.
19
Milliman
Research Report
First we adjusted the MEPS population by re-weighting it such that it represents a typical commercially
insured population. We selected a set of conditions for testing correlation of a BMI calculation with the
incidence of these conditions. The conditions were selected based on their relevance to the build of a
person using a combination of clinical input and experiential judgment. The conditions that are considered
include cancers (larynx, lung, and oral cavity), chronic obstructive pulmonary disease, leukoplakia, colitis,
diabetes mellitus, hypertension, prostatitis, and asthma.
We found that Rohrer’s index
improves linear correlation
with both overall healthcare
costs and incidence of select
conditions that are more likely
to be influenced by the build
of a person.
We found that Rohrer’s index improves linear correlation with both overall healthcare costs and incidence
of select conditions that are more likely to be influenced by the build of a person.
Figure 24: Correlation Coefficients of RI and BMI with Overall Healthcare Costs
All Healthcare Costs
Rohrer’s Index (RI)
0.17
Body Mass Index (BMI)
0.14
ACKNOWLEDGEMENT
We would like to thank the friendly and helpful personnel at the Medical Expenditure Panel Survey data
department, especially Ray Kuntz who helped us with data for this study.
Caveats and limitations
Milliman makes no representations or warranties regarding the contents of this report to third parties.
Likewise, third parties are instructed that they are to place no reliance upon this report that would result
in the creation of any duty or liability under any theory of law by Milliman or its employees to third parties.
Impact of height, weight, and smoking on medical claim costs
Research from the annual update of Milliman’s Medical Underwriting Guidelines
Jonathan Shreve, Mary van der Heijde and Tom Attaway
April 2009
20
1301 Fifth Avenue, Suite 3800
Seattle, WA 98101
(206) 624-7940
www.milliman.com