V401 Case 2: An Economic Evaluation of “Safer Choices”

QUESTION

V401
Case 2: An Economic Evaluation of “Safer Choices”

Overview

School programs to reduce unprotected
sexual intercourse have been implemented across the United States, to reduce
human immunodeficiency virus (HIV) infection, infection by other sexually
transmitted diseases (STDs), and unintended pregnancy among US adolescents. Program evaluations have shown that such
programs are effective in reducing unprotected sexual intercourse, substantially
increasing condom use and other forms of contraceptive use among sexually
active young people. Because resources to fund school-based HIV, other STDs,
and pregnancy prevention programs are limited, however, program effectiveness
is not sufficient to justify program implementation. Issues of practical
concern to policy makers are financing (whether prevention programs are
affordable) and whether the benefits of such programs exceed their costs.

Few economic studies of school programs
have been conducted. Most studies of HIV
prevention programs in particular have focused on those targeting intravenous
drug users, adult urban women, and adult or adolescent gay and bisexual men.
Moreover, few studies have assessed the reduction of STDs other than HIV, or
have looked at the benefits and costs ofpreventing unintended teenage pregnancy.
This case tries to get a better sense of the benefits and costs of a program to
reduce the amount of unprotected sex, by monetizing the multiple benefits such
programs can offer.

This topic is particularly policy
relevant since sexually active people between the ages of 15
and 24 accounted for 50% of all new cases of sexually transmitted diseases in
the United States. Researchers found about 18.9 million new STD cases occurred
in 2000, for example, and 9.1 million of the cases, or roughly 50%, occurred
among people between the ages of 15 and 24. This age group also accounts for the majority
of unintended pregnancies, and a significant fraction of new HIV infections.

The Data and NPV Computations

Tables 1 and 2 give the basic
economic data needed to compute probability distributions for the net present
value of the “Safer Choices” program.
The question to be answered is whether it is probable that the costs of
the program are less than the expected benefits, and thus, whether such a
program should be instituted widely by local school districts, assuming the
financial resources are available to implement such a program. We will return
to financing issues in the next section.
Costs include the usual
inputs of time and materials needed to institute this kind of program. The
benefits fall into the following categories:
reduced
medical costs for treating STDsreduced
medical costs for dealing with unintended pregnanciesincreased work productivity from reducing the
incidence of HIV infectionReduced
medical costs associated with reducing the incidence of HIV infection. See
Table 2 for the particular details.
Not included on this list is the
quality-of-life improvement received by someone who otherwise would have contracted
STDs, had an unintended pregnancy, or contracted HIV. Since it is hard to
monetize the benefit “quality of life improvement,” we leave it out of the
quantitative assessment. However, such a benefit might affect your decision
about recommending the program, even though it cannot be explicitly considered
in the quantitative analysis.
Similarly, the “value of
live” is not monetized in this study, for two reasons. First, the assumption
that a person who now contracts HIV will live close to a normal life span,
given dramatic improvements in the efficacy of
antiretroviral drugs. In this particular
case, we assume that life spans are 10 years shorter for the person who
contracts HIV. Secondly, monetizing the value of life is a controversial issue in
cost benefit analysis. But note that by not monetizing the value of life, the
approach taken here, a downward bias is imparted to our estimate of the
economic value of the program – that is, our estimate of the program’s benefits
will be conservative – because extending lifespans 10 years definitely has
value. This issue can also be considered qualitatively in your assessment of
the program.
Overall, the question is what
is the probability that the net economic value of Safer Choices is positive,
given the data in Tables 1 and 2? Given
the information you have, can you recommend with reasonable confidence that
Safer Choices provides positive net-benefits?

Distributional and Financial
Analysis
We now
consider how Safer Choices might be financed. One option would be to have the
taxpayer finance the program. KHT1 below shows the distributional effects of
Safer Choices when taxpayers finance the program. The table reflects the fact
that insurance companies receive the fraction “a” of medical cost savings on
the assumption that insurance companies would otherwise have paid the fraction
“a” of these costs. The student beneficiary of the program receives the
fraction (1-a) of the benefits – effectively, the value of the savings in
copayments they would otherwise have had to have been paid had the program not
benefited the student in one of the possible ways it might. On the assumptions
that (a) the cost of the program is completely covered by taxpayers and (b) the
employers gain in productivity just equal the wages they would pay out, we can
see that Safer Choices benefits two groups: (a) the students who use it and
benefit from it: they gain a lifetime stream of co-payment savings, plus additional wages from additional work and
(b) insurance companies, who get a lifetime stream of savings in reimbursements
for medical expenses. To get a sense of
the magnitude of the benefits to these two groups, numerically specify KHT1 based
on the mean npv estimates for the stakeholders in question. Choose the means
from either the college educated or non college educated npv
distributions.
A second
KHT should be specified for an alternative financing arrangement whereby
insurance companies pay schools to adopt “Safer Choices” (see KHT2). Again
choose means from either college educated or non-college educated distributions
to specify the KHT
Note
that if the financial returns to health insurance companies from avoided future
reimbursements are higher than the subsidy they would have to pay to school
districts to institute safer choices, it would be in their interest to do so.
It is for this reason that many employer-provided insurance program, such as
the TIAA-CREF programs which cover IU faculty, reimburse preventive care at
100% in the hopes that people will live healthier lifestyles — reducing health
insurance payouts later down the road. In the same way, it might make sense for
insurance companies to pay schools to encourage students to lead healthier
lifestyles.
Note:
you can assume that “a”= .8, and therefore that “1-a”= .2 In short, students
affected by the program (or their parents) would normally make a 20% copay,
with health insurance companies covering 80%.
Case Outputs
The
basic output is a no longer than a 3
page double-space memorandum (this page length EXCLUDES ANY TABLES YOU MIGHT
INCLUDE) that describes the analysis, presents the results, and then makes a
recommendation whether or not Safer Choices is a program local school districts
should promote, with also a recommendation about the financing mechanism. As
with the pervious case, your memo should be broken down explicitly into
sections with bolded headers as follows:

Introduction
Analysis Method and
Assumptions
Results
Policy Recommendation

Your
assessment should be based on four figures/tables.
Figure 1
shows the NPV distribution of the program for non-college educated students,
with the summary statistics @Risk provides (mean, standard deviation, minium,
maximum, etc.) Also, do the overlay that shows the kind of distribution
generated.
Figure 2
shows the NPV distribution of the program for college-educated students, with
the summary statistics @Risk provides (mean, standard deviation, minimum,
maximum, etc.) Also, do the overlay that shows the kind of distribution
generated.
Table 3
is KHT 1 based on the mean estimated from the distributions in either Table
1 or Table 2.
Table 4
is KHT2 based on the means estimated from the distribution you chose for KHT1. That
is, do either non-college educated or college-educated for both the KHTs.

Ground Rules
As in The
previous case, you are encouraged to form working groups and collaborate on the
analysis. But you must write up the memo yourself.

Let me know
if you have any questions.

Table 1: Program costs per 1000 students in initial period
(period zero)

Cost Category

Cost

Teacher training

59000

Teaching

36153

Peer facilitators

53166

Site coordinator training

48913

Site coordination

12121

Curriculum packages

8000

Implementation manuals

1063

Activity kits

1250

Photocopies for students

58

Photocopies for teachers

64

Videos

600

Total

220388

Table 2: Program Benefits

Cost Category

Value
and Time Period

Cases Avoided per 1000 students

Avoided medical cost of
STD treatment (a weighted average of bacterial STDs (Chlamydia, Gonorrhea,
PID) and viral STDs such as Herpes

Lognormal distribution
Mean $75 per case avoided,
Sigma=$5

realized every year for 51 years, starting in program year
(period 0)

PERT
Low=0
Medium=12
High =15

Avoided cost of one unwanted pregnancy (a weighted combination
of cost of live birth, prenatal care, or pregnancy termination)

Lognormal distribution
Mean=$5,200 per avoided
pregnancy,
Sigma=$900

realized in the program year, and the following year (Periods 0
and 1)

PERT
Low=0,
Medium=2
High=6

Value of work productivity gain from avoiding HIV — non college
educated

Normal distribution:
Mean=$14.00 per hour
Sigma=$2.00 per hour

2000 hours per year over
40 years starting in year after program commences (period 1)

Lognormal
Mean=2
Sigma=.4

Value of work productivity gain from avoiding HIV — college
bound

Normal distribution
Mean= $18.00/hour
Sigma=$3/hour

2000 hours per year over
40 years starting in the 4th year after program commences, for 36 years
(starting in period 4)

Lognormal
Mean=2
Sigma=.4

Cost estimate of value of medical cost savings from avoiding HIV (savings in
antiretroviral drugs and other treatments)

Normal distribution
Mean=$15,000 per case avoided per year
Sigma=$1000

Realized each year for 40 years

Lognormal
Mean=2
Sigma=.4

KHT1-Taxpayer
financed

Students

School
Administrators

Employers

Tax Payers

Insurance
Company

Net

Avoided medical costs of STDs

(1-a) B1

aB1

B1

Avoided medical
costs of unintended pregnancy

(1-a) B2

aB2

B2

Avoided
Medical costs of HIV

(1-a) B3

aB3

B3

Benefits of improved quality
of life/ length of life

Worker
Productivity Gain from reduced HIV incidence

B5

B5

Program Cost

-C1

-C1

T1

-T1

0

T2

-T2

0

Net

(1-a)[B1+B2+B3]
+T2

T1-C1=0

B5-T2=0

-T1

a[B1+B2+B3]

B1+B2+B3+B5-C1

KHT2-Insurance
Company Financed

Students

School
Administrators

Employers

Insurance
Company

Net

Avoided medical costs of STDs

(1-a) B1

aB1

B1

Avoided
medical costs of unintended pregnancy

(1-a) B2

aB2

B2

Avoided
Medical costs of HIV

(1-a) B3

aB3

B3

Benefits of improved quality
of life/ length of life

Worker
Productivity Gain from reduced HIV incidence

B5

B5

Program Cost

-C1

-C1

T1

-T1

0

T2

-T2

0

Net

(1-a)[B1+B2+B3]
+T2

T1-C1=0

B5-T2=0

a[B1+B2+B3]-T1

B1+B2+B3+B5-C1

 

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