Online-Only Appendix
The ADA Risk Score
The ADA score is calculated as follows: BMI  27 (5 points), under age 65 and get little
or no daily exercise (5 points), between 45 and 64 years old (5 points), over 65 years (9 points),
gestational diabetes (1 point), sibling with diabetes (1 point), parent with diabetes (1 point). We
did not have information on whether respondents had ever developed gestational diabetes. We
calculated the ADA risk score using two different measures of exercise frequency that were
based on answers to questions in the survey. The two scores were similar, and we report only one
here.
Self-assessed diabetes risk
Our survey first described general diabetes risk factors. After reading these, respondents
were asked to provide their self-assessed lifetime risk of developing diabetes and their risk of
developing diabetes in the next 3 years. The survey questions focused on 3-year risk,11 which
was elicited by using the following question along with graphical representations of risk levels:
3-Year Risk: You can also think of the risk of getting diabetes in the shortterm, like 3 years. Research studies show that on average, 30 out of 1,000 people
(3%) will get diabetes some time in the next 3 years. However, among the highest risk
group, 300 out of 1,000 people (30%) will get diabetes in the next 3 years.
What do you think are your own chances of getting diabetes some time in the
next 3 years compared to the average chances of 3% (30 out of 1,000)?

Much higher than the average (about 30% risk of getting diabetes)

Higher than the average (about 15% risk of getting diabetes)
1

About the same as the average (about 3% risk of getting diabetes)

Lower than the average (about 2% risk of getting diabetes)

Much lower than the average (about 1% risk of getting diabetes)
Trade-off questions
Our survey instrument elicited respondents’ willingness to accept trade-offs among
intervention attributes. It presented respondents with a series of nine choices between pairs of
hypothetical risk-reduction programs and a no-participation alternative (see figure). Each
program consisted of seven program attributes. The no-participation alternative allowed
respondents to choose not to enroll in either of the two hypothetical diabetes-prevention
programs offered in each question. Such a choice would result in their maintaining their current
diet, exercise level, and baseline risk of developing diabetes, and there would be zero additional
cost. A set of program attributes defined each hypothetical program, including dietary
restrictions, exercise requirements, number of counseling sessions, medication to reduce the risk
of developing diabetes, weight loss goals, monthly out-of-pocket cost of the program, and a
specified reduction in the risk of developing diabetes.
Statistical methods
The data form a time-series/cross-section panel that can be analyzed using stochastic
utility maximization theory. We estimated program preferences with conditional logit models,
which assume that the utility associated with a particular choice alternative is expressed as a
function of individual characteristics and the attributes of the alternative. Individual indirect
utility has deterministic and stochastic components. The deterministic part is a function of
program attributes and personal characteristics:
2
Uijt  Vi (X jt , Zi , p jt ; i , i )  eijt ,
(1)
where
Ujti is individual i’s utility for a program, where j = 0, 1, 2, denotes the three alternative
programs in each choice set, and t = 1,…,9, representing the nine trade-off questions;
Vi() is the nonstochastic part of the utility function;
Xjt is a vector of attribute levels for the program;
Zi is a vector of personal characteristics;
pjt is the cost of the program;
i is a vector of attribute parameters;
i is the marginal utility of money; and
ejti is a disturbance term.
The linear specification of utility for the three alternatives is
U ijt  Vjti  eijt   i0  eijt j  0
U ijt  Vjti  eijt  X jt i  p jt i  eijt
j  1, 2
where Ujti , j = 0, 1, 2 is the utility of each of the three program alternatives. U0ti is the utility of
the no-participation choice, which in a simple model is just 0, an alternative-specific constant
for the no-participation choice. The utility of Program A is U1ti , and the utility of Program B is
U2ti .
Stochastic utility maximization asserts that individual i will choose alternative j from
among the full set of available alternatives K if, and only if, alternative j provides a higher
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(2)
overall level of utility than all other alternatives in the choice set.a Assuming the disturbance
term follows a Type I extreme-value error structure, the probability that alternative j will be
selected from choice set t is the standard conditional logit expression:
Pr ob[Cit  j] 
 
∑exp  V 
exp Vjti
2
(3)
i
kt
k 0
where Cit is the selected alternative in each of six choice sets and Vjti is the determinate part of the
utility of alternative j.b The probability that an alternative will be selected is the ratio of the
exponentiated utility that the particular alternative provides, relative to the exponentiated sum of
the utilities that each alternative in the choice set provides. Individual characteristics do not vary
among choices and thus must be interacted with program attributes or alternative-specific
constants. The conditional logit model specified by Eqs. (2) and (3) is estimated using maximum
likelihood. That is, given the characteristics of the alternatives in the choice sets presented to the
respondents, the model estimates coefficients that maximize the likelihood that we would
observe the actual choices in the sample. Thus, the coefficients show the relationship between
the probability of selecting a program and the attributes of that program.
Once we have estimated the utility functions, we can determine the effect of changes in
various attributes on individual utility. We also will be able to place a monetary value on
a
Mathematically, individual i will choose alternative j from among the set of alternatives K,
if Ujti > Ukti for all j in K, j ≠ k
substituting for Ujti from Eq. (1), and rearranging terms we have
Vjti – Vkti > ekti – ejti .
b
The basic exposition of the properties of this model can be found in McFadden (1981).22
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0
changes in utility. Let X j represent the no-participation vector of attribute levels. X*j represents a
different vector of attribute levels. The WTP for a given change in commodity attributes (X*j –
X0j) is the amount of money (p*j – p0j) that would leave respondent i indifferent between paying
for the change in attribute levels or remaining in the no-participation state at no cost.
Mathematically, this is the level of p*j that satisfies
Vi[X*j, Zi, p*j;i, i(p, Zi)] = Vi[X0j, Zi, p0j; i, i(p, Zi)].
(4)
The negative of the estimated coefficient on the cost term (–) can be interpreted as the
marginal utility of income (i.e., the utility derived from having additional dollars). Therefore,


WTP i X*  X 0  p*j  p0j 
X
*

 X 0 i
 δi
.
(5)
We used the conditional logit models to calculate the change in utility associated with
reducing the risk of developing diabetes according to Eq. (5). We report predicted uptake rates
for each program separately. After estimating the parameters, we estimated Krinsky-Robb23
confidence intervals for WTP for risk-reduction programs. The Krinsky-Robb procedure
involves drawing 10,000 simulations from the joint multivariate normal parameter
distribution. For each simulation, we then calculated mean WTP conditional on choosing the
program over the status quo. That is, we calculated realized or ex post WTP for each draw rather
than expected or ex ante WTP. To calculate 95% confidence intervals, we sorted the resulting
10,000 estimates and recorded the values at position 250 and 9750.
Self-assessed risk
The table shows diabetes risk characteristics by level of perceived risk.
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Table. Diabetes Risk Characteristics, by Level of Perceived Risk
Characteristic
Full Sample
High Risk
Low Risk
582
157
425
22 (3.8)
9 (5.7)
13 (3.1)
Exercise at least 3 hours per week
135 (23.2)
24 (15.3)
111 (26.1)
Doctor has indicated diabetes risk
170 (29.2)
85 (54.1)
85 (20.0)
Have been on diet in last 2 years
344 (59.1)
100 (63.7)
244 (57.4)
$15.53; $48.33
$16.39; $45.12
$15.21; $49.72
(32.1)
(36.3)
(30.6)
N
Have taken a class about reducing
diabetes risk*
Monthly personal costs associated
with diet, exercise, counseling, and
medication†
Figures indicate number of respondents (percentage of respondents), unless otherwise indicated.
*Two low-risk individuals were not sure if they had taken such a class.
†
Overall mean; mean for those giving nonzero answers (percentage giving nonzero answers).
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Figure. Sample Stated-Choice Task
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8
Figure. Sample Stated-Choice Task
Program A
Program B
NEITHER
Restricted diet
Flexible low-calorie diet
I will not enroll in
3 hours of exercise per week
6 hours of exercise per week
either of these
Program
No counseling
16 sessions of counseling
programs.
Features
No medication
Medication
Goal: Lose 20 lbs in 1 year
Goal: Lose 40 lbs in 1 year
$25 per month for 3 years
$200 per month for 3 years
I will reduce my risk of
I will reduce my risk of getting I will maintain my
getting diabetes in the next 3 diabetes in the
current 30% risk of
years from 30% to 21%.
next 3 years from 30% to
getting diabetes in the
15%.
next 3 years
Program
Benefits
21/100
15/100
30/100
people will
people will
people
get diabetes
get diabetes
will get
diabetes
Which
Program do
Prefer A
Prefer B
X


you prefer?
(Please
check one
box.)

If you chose A or B:
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Prefer Neither

How likely is it that you actually would
Very likely
follow the diet, exercise, and counseling requirements for the
Fairly likely
program you chose above?
Not very likely
(Please check one box.)


X

In this example, the person indicated that Program B is preferable to Program A and that he or
she would be “Fairly Likely” to follow Program B.
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