Scandinavian Journal of Surgery 101: 190–197, 2012
BUDGET IMPACT ANALYSIS OF SURGICAL TREATMENT
FOR OBESITY IN SWEDEN
S. Borg1, I. Näslund2, U. Persson1, K. Ödegaard1
1
2
The Swedish Institute for Health Economics (IHE), Lund, Sweden
University Hospital Örebro, Surgical department, Örebro, Sweden
ABSTRACT
Background: The recent substantial increase in the number of obese surgeries performed
in Sweden has raised concerns about the budget impact.
Objective: Our aim in this paper is to present an assessment of the budgetary impact
of different policies for surgical intervention for obese and overweight subjects from a
healthcare perspective in Sweden.
Methods: The model simulates the annual expected treatment costs of obesity related
diseases and surgery in patients of different sex, age and Body Mass Index (BMI). Costs
evaluated are costs of surgery plus the excess treatment costs that an obese patient has
over and above the treatment costs of a normal-weight patient. The diagnoses that are
included for costs assessment are diabetes and cardiovascular disease since these diagnoses are the principal diagnoses associated with obesity. Four different scenarios over
the number of surgical operations performed each year are simulated and compared: (1)
no surgical operation, (2) 3 000 surgical operations in persons with BMI > 40, (3) 4 000
(BMI > 40), and (4) 5 000 (expanded to BMI > 38).
Results: Comparing Scenario 2 with Scenario 1 results in a net budget impact of on
average SEK 121 million per annum or SEK 40 000 per patient. This implies that 55 percent of the cost of surgery, set equal to SEK 90 000 for each patient, has been offset by a
reduction in the excess treatment costs of obesity related diseases. Expanding annual
surgery from 3000 to 4000 the cost-offset increased to 58%. By expanding annual surgery
further from 4000 to 5000 and at the same time expanding the indication for surgery from
BMI > 40 to BMI > 38, no cost-offset is obtained.
Conclusion: A cost-minimization strategy for bariatric surgery in Sweden should not
expand indication, but rather increase the number of surgeries within the currently accepted indication.
Key words: Budget impact analysis; health economics; simulation model; obesity; surgery, open model
Correspondence:
Sixten Borg, M. Sc.
The Swedish Institute for Health Economics (IHE)
Box 2127
SE - 220 02 Lund, Sweden
Email: sb@ihe.se
Budget impact of gastric bypass surgery
INTRODUCTION
The rising trend in obesity has become a heavy cost
burden for the health care delivery system in many
countries. A frequent used marker for obesity is the
Body Mass Index (BMI), defined as the ratio of the
weight of a person in kilo to the length of the person
in meter squared (kg/m2). The WHO uses this marker
and defines a person as obese when BMI is greater
than or equal to 30 (1). Persson U et al estimates the
health care costs and the indirect costs, i.e. cost for
short term sick leave, early retirement and death before retirement, attributable to overweight and obesity in Sweden to SEK 3 575 million and SEK 12 516
million for the year 2003 representing about 2% and
4% respectively of the total health care costs and the
total indirect costs for the country (2–3). Similar costs
estimates have been presented for a number of other
countries (4–6). In the US with a much higher prevalence of overweight and obesity, the health care costs
associated with overweight and obesity is estimated
to account for as much as some 6% of total health care
costs (7).
There are basically three different treatment options for obesity: diet and exercise therapy, pharmacological therapy and surgery.
Following great improvement in safety and efficacy of surgery as an alternative for obesity treatment, the demand for surgery has in recent years
increased rapidly. Obese surgery is, however, quite
expensive and this has raised concern in Sweden
about budgetary impacts of an increased demand for
surgical treatment of obesity. A Swedish weekly medical paper, recently reported that to offer surgical
treatment to all Swedes with BMI > 35 would cost
some SEK 18 000 million an amount equal to cost of
the annual supplies of pharmaceutical products to
the national health care delivery system (8). This estimate, however, overestimates the budgetary impact
because it only counts the costs and not the monetary
value of the benefits that can be expected to accrue
with surgical intervention for obesity.
191
Budget impact analysis (BIA) has become an essential part of a comprehensive economic assessment
of a health care technology along with cost-effectiveness analysis (CEA), prior to formulary approval or
reimbursement. BIA is not a substitute for CEA but
should be viewed as a complementary analysis.
Whereas CEA evaluates the costs and outcomes of
alternative therapies over a specified time horizon, a
BIA addresses the financial stream of consequences
related to the uptake and diffusion of new therapies
to assess their affordability over time (5).
OBJECTIVE
Our aim in this paper is to present an assessment of
the budgetary impact of different programmes for
surgical intervention for obese and overweight subjects in Sweden.
METHODS
In Fig. 1 we illustrate our approach for measuring and assessing the monetary benefit of surgical treatment of obesity. In panel 1 we have drawn in two curves showing the
distribution of the obese population before and after surgery and one curve showing the expected cost of treatment
per patient for obesity related diseases. Note that the latter
curve has a sharply exponential shape that increases with
the severity of obesity. The shape of this curve is in accordance with the finding reported by Persson U et al (2). With
surgery there will be fewer severely obese people with
higher BMI values and as result the total treatment costs of
obesity related diseases illustrated in panel 2 will twist and
become shifted to a new position which is likely to result
in a net monetary benefit with area B smaller than area A.
will examine the potential monetary benefit illustrated
18We
in Fig. 1, using a budget impact model (BIM).
BIM
A BIM is commonly presented as either an open model or
closed model. The two models differ in how they define
panel 1
# of
obese
panel 2
costs per
patient
after
surgery
Total
costs
B
before
surgery
after
surgery
before
surgery
costs per
patient
A
BMI = 30
BMI
BMI = 30
BMI
Fig. 1. Principal illustration of the distribution of BMI in the obese population before and after surgery (panel 1), and the cost of treatment
of obesity related diseases per patient as a function of BMI (panel 1), and a principal illustration of the source of total costs from obese
persons with different BMI values, before and after surgery (panel 2).
Figure 1: Principal illustration of the distribution of BMI in the obese population before and
after surgery (panel 1), and the cost of treatment of obesity related diseases per patient as a
S. Borg, I. Näslund, U. Persson, K. Ödegaard
192
and measure the size of the target population for which
they seek to estimate the budgetary impact of an intervention. In a closed model the budget impact is estimated for
a given group of people/a cohort with given set of characteristics. For this group the model simulates the outcomes
in terms of the events and the consequent budgetary impacts that the group will generate in the course of the simulation run. A major shortcoming of a closed model is that
it fails to account for new cases of diseases in persons that
were not members of the group at the start of the simulation run. An open model does not have this shortcoming.
The incidence of new cases that will occur over the time
horizon of the simulation and for which the intervention
will take place, are accounted for. Drawing on the general
guidelines for budget impact analysis as presented in a
working paper from International Society for Pharmacoeconomics and Outcomes Research (ISPOR) (9), we have developed an open model.
Our model is a micro-simulation model that simulates
the budgetary impact with surgical treatment of an obese
patient. The underlying methodology is that of a Markov
model. At the start of the simulation run, a population of
overweight and obese persons (BMI > 25) is sampled from
a data set representing the adult Swedish population (defined as ≥ 15 years old). These values are subsequently updated each year as follows:
• Each person’s BMI value is updated to account for an
annual change/up-drift in BMI
• Parameters specify the number of surgical operations to
be done each year, and the model will randomly draw
eligible candidates in the population for surgery, eligibility criteria BMI above a given threshold (see below), and
no prior surgery received. The BMI value is subsequently adjusted for the effect of surgery.
• New overweight persons enter the model, by moving
from having normal weight to become overweight. It is
assumed that all new cases of overweight persons just
pass the threshold value of BMI = 25, and their age and
gender are randomly sampled from the corresponding
Swedish subpopulation.
• New 15-year old persons enter the model, to represent
children than become adult. They are randomly sampled
from the population of 15-year old Swedes, with regards
to gender and BMI.
• Each person’s survival is evaluated based on a risk of
death determined from age, gender and BMI, and in addition, a one-time risk of death in connection to surgery
in those given surgery.
• The costs accrued by each person is derived from two
different sources: (1) surgery procedure costs and (2)
expected excess costs of treatment of obesity related diseases. The excess cost for a person with a BMI value
greater than 25 is defined as the difference in cost of
treatment for obesity related diseases between a person
with that BMI value and a normal weight patient (20 ≤
BMI < 25). With surgery a patient’s BMI is reduced and
consequently the excess treatment costs also decline.
For budget impact analysis we used a 10 year time horizon
and ran 4 different simulation scenarios using Monte-Carlo
simulation techniques, of the annual number of surgical
interventions performed: 1) no surgical interventions; 2)
3000 surgical interventions; 3) 4000 surgical interventions
and 4) 5000 surgical interventions, and at the same time
expanding the BMI range for being eligible for surgery,
from BMI > 40 to BMI > 38.
MODEL INPUT DATA
Population characteristics
The characteristics of each patient are drawn from the adult
(age 15–84) Swedish population. In Fig. 2 we report the
distribution of the overweight19
and obese population by
age, gender and BMI (10). The initial number of persons
19
with obesity (prevalence)
is 785 thousand (74% mild, 19%
moderate and 7% severe obese). We apply WHO´s classification and define mild obese to be those with BMI in the
range 30 to 35, moderate obese to be those with BMI in the
range 35 to 40 and severe obese for those with BMI greater
or equal to 40. Overweight persons are those with BMI in
Mild obese
Moderate obese
Overw
eight
Severe
obese
Age
Mild obese
Moderate obese
Severe obese
75-84
65-74
55-64
45-54
Fig. 2. The model’s initial population pyramid, showing the number of overweight, mild, moderate
and severe obese persons by age
group (15-24, 25-34, et c.) and gender.
Notes: The horisontal bars represent number of persons in each
age-gender group, extending to
the right (male), and to the left (female) using a horisontal scale in
thousands of persons, originating
from the vertical line.
35-44
25-34
15-24
4'
3'
2'
Male
1'
0'
1'
2'
3'
4'
Female
Figure 2: The model's initial population pyramid, showing the number of overweight, mild,
moderate and severe obese persons by age group (15-24, 25-34, et c.) and gender.
Budget impact of gastric bypass surgery
the range 25 to 30. The model is set to draw candidates for
surgery as 80% women and 20% men to reflect current
practice (11).
Obesity-related mortality
In the medical literature the mortality rate of the obese
population is reported to be about twice as high as for the
general population of corresponding age and gender (12–
14).
To account for the risk of death we use the age and gender specific general mortality risk in the Swedish general
population, and in order to account for excess mortality
due to obesity, we accelerate the general mortality using
gender and BMI in accordance with published data from
the Prospective Studies Collaboration (13).
In addition to these mortality risks, a patient undergoing
surgery faces an additional risk of death. From the Swedish Obesity Study (SOS) it has been reported that 1 person
died for every 500 surgeries performed (15). For our model
estimate we apply this mortality risk for patients undergoing obese surgery.
Obesity drift
Obesity drift is a measurement of how a persons weight
changes with age. Drawing on the finding from a large
study of socioeconomic status and obesity growth by Baum
CL and Ruhm AI (16), we set the drift equal to +0.12 BMI
per annum for age < 45. Unfortunately the study only reports BMI growth for age below 40, but they report that the
growth curve has a concave shape. Using prevalence data
of obesity of different age group and corresponding mortality data from Flegal KM et al (14), we estimate the BMI drift
to be +0.07 BMI per annum for people in the age group 45
– 65, and –0.17 BMI per annum (i.e. negative) for people
with age > 65.
Obesity surgery efficacy
For our model estimate we use the efficacy as reported by
gastric by-pass technique from the Swedish obesity study
(SOS) (17). In the first year following gastric-bypass surgery
on 265 patients the average weight loss was 32%. A gradual increase in weight gain was, however, observed after the
initial year. During a 15-year period the average weight loss
remained between 20–30%. For our model estimate we as-
193
sume an average weight loss of 27%. In order to avoid
overestimation of the benefit from a BMI reduction in terms
of survival and costs, we adjusted the reduction above according to a maximum fraction of benefit, i.e. the modelled
patient has an actual BMI value, reduced by 27% by surgery. This value is used for describing the patient’s level of
obesity. In addition, the patient has an adjusted BMI value
which is gradually reduced over three years time from the
point of surgery, to reflect 75% of the actual weight reduction by the end of the three years. After this point, the adjusted BMI value, as well as the actual BMI value, are updated annually using the Obesity drift presented above.
The patient’s costs (reflecting the need for healthcare) and
mortality are determined from the adjusted BMI value. The
maximum fraction and the time period were estimated
from the economic outcome seen in the Swedish Obesity
Study (SOS).
Treatment cost of obesity-related diseases
To capture the costs as experience by Swedish health care
providers, we use data from a study that have analysed the
development in costs of treatment of obesity related diseases (2). The diseases covered in this study included diabetes, hypertension, angina, myocardial infarction and
stroke caused by hypertension. Using the so-called population attributed risk approach, this study reports the excess
cost attributed to overweight and obesity for the health care
providers to SEK 1,369 million for men and SEK 1,598 million for women in 2003 years prices (2, Table 4.3). In Table
1 we have converted these cost estimates to per capita estimates and updated them to the 2006 price level. For our
purpose the important point to note in Table 1 is that the
excess cost per capita increase rapidly with the severity of
obesity.
The model assigns annual excess costs to patients according to their current BMI, age group and gender. For persons
with normal weight, the excess cost is by our definition
zero, and for persons with overweight (25 ≤ BMI < 30) we
use the average costs estimates as reported in Table 1. For
obese patients (BMI ≥ 30) we fitted the following equation
using the data in Table 1 to obtain an excess cost for each
BMI value, for men and women, with age < 55 and age ≥ 55:
Excess cost = α + β(BMI-30)2. The values of α for men and
women, with age < 55 and age ≥ 55, respectively, are found
in Table 1 in the column for BMI 25–29.9 (e.g. 297 for men
age < 55). The β coefficient for men aged < 55 is 80, and 162
for age ≥ 55. The corresponding β values for women are 45
and 79.
TABLE 1
Per capita excess treatment costs obesity related diseases, SEK per year.
BMI
Age
Men
Women
Men
Women
25–29,9
> = 30
30–34,5
35–39,9
> = 40
12 928
26 137
9 026
16 270
13 225
26 739
9 234
16 644
age < 55
age > 55
age < 55
age > 55
291
633
505
1 107
per capita excess costs, SEK 2003
1 590
1 177
2 881
3 102
2 379
5 825
1 664
964
2 161
2 800
1 738
3 895
age < 55
age > 55
age < 55
age > 55
297
647
517
1 132
per capita excess costs, SEK 2006
1 627
1 204
2 947
3 173
2 433
5 959
1 702
987
2 211
2 864
1 778
3 985
Source: Persson U et al (3).
194
S. Borg, I. Näslund, U. Persson, K. Ödegaard
Surgical treatment costs
Cost of surgical treatment varies among Swedish hospitals.
Our model uses the findings from a Swedish study that
report the cost to be SEK 70 000 (18). As an assumption, we
increased this cost to SEK 90 000 to account for costs for
follow-up plastic surgery that may be needed.
RESULTS
The number of surgical interventions reported in
Table 2, reflect the aim to enrol 20% men and 80%
women for surgery, and that aim was met with Scenario 2 and Scenario 4. For Scenario 3 at the end of
the simulation run, nearly all severe obese men had
received surgery so that less than 20% men could be
found to be eligible for surgery. Instead a higher percentage of women were selected for surgery.
The effect of surgery, in terms of resulting number
of obese persons after ten years, is shown in Fig. 3
along with the initial state of the population. All surgery scenarios reduced the number of severe obese
subjects (Table 2). With all scenarios, the number of
mild obese persons increased, but more so in the ones
with surgery because some patients with reduced
BMI are transferred into the mild obese group. With
scenario four, which has an extended indication (BMI
38 as threshold), the reduction in severe obese was
less than with the other strategies. This was because
a number of moderate obese was selected for surgery
which reduced the number of severe obese selected
for surgery. Another important point to note in Fig. 3
is that the number of obese persons increased over
ten years time with all scenarios except the fourth.
The reason for this result is that we let new obese
subjects enter the model each year, as we are using an
open model.
We measure the net cost/budget impact per annum which we define as the difference in average
annual costs between Scenario 1 and Scenarios 2, 3
and 4 respectively. For Scenario 2 we estimate the net
cost/budget impact to SEK 121.3 million, The corresponding net cost/budget impact for Scenario 3 and
4 we estimate to SEK 152.6 million and SEK 247.5
million respectively.
Another important point to note is that with surgery more than half the cost of a surgical treatment
per patient, SEK 90 000, is offset by a change/reduction in excess healthcare costs for treatment of obesity
related diseases (Table 3). The cost offset we defined
as the difference in cost of surgery per patient and the
total costs per patient. For Scenario 2 the simulation
result gave a total cost per patients of SEK 40 410
implying a cost-offset of SEK 49 590. Expressed as a
percentage of the cost of surgery, this corresponds to
a cost offset of 55.1%. The corresponding percentage
cost offset with Scenario 3 and 4 is 57.6% and 45.0%
respectively. The reason for the decline with Scenario
4 is that in this scenario we have also expanded the
indication for surgery to include non-severe obese
patients. This is a consequence of how the excess
costs of obesity related diseases increase with higher
BMI values, implemented in our model as annual
excess cost functions described above. For less severe
obese patients the excess cost of obesity related diseases is less than for more severe obese persons, and
hence there is less potential to reduce the excess cost
of treatment of obesity related diseases.
Also an interesting point to note is the development in the incremental cost-offset ratio. Comparing
Scenario 3 with Scenario 2 that is an increase in the
number of annual surgeries from 3 000 to 4 000,
showed a net cost-offset of 65.2%, but expanding further from 4 000 surgeries to 5 000 surgeries and a
wider BMI range, no further cost-offset is obtained.
In fact it becomes slightly negative, as the net cost per
patient becomes larger than the cost of a surgical procedure.
TABLE 2
Total number of surgical operations over ten years in Sweden, initial and resulting number of obese persons after ten years (thousand persons),
with each of the four scenarios.
Scenario
Number of surgical operations per year
Threshold for surgery
1
2
3
4
0
–
3 000
40 kg/m2
4 000
40 kg/m2
5 000
38 kg/m2
Total number of surgical operations over ten years
Male patients
Female patients
0
0
6 000
24 000
07 700
32 300
10 000
40 000
Total
0
30 000
40 000
50 000
Number of obese subjects (initially*)
Mild obese (578)
Moderate obese (151)
Severe obese (57)
596
150
57
611
154
33
618
154
22
612
135
32
Total (785)
803
798
793
779
Notes
* At start of the simulation.
20
Budget impact of gastric bypass surgery
Moderate obese
Mild obese
400
0
200
Thousand persons
600
800
Severe obese
195
Initially
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Fig. 3. The total number of mild, moderate and severely obese persons initially in the model (leftmost), and after ten years according to
each of the scenarios 1 to 4.
Notes: Figure 3: The total number of mild, moderate and severely obese persons initially in the
Scenario 1 = no surgery; Scenario 2 = 3 000 operations/year in patients with BMI over 40 kg/m2; Scenario 3 = 4 000 operations/year in
patients with BMI over 40 kg/m2; Scenario 4 = 5 000 operations/year in patients with BMI over 38 kg/m2.
model
and
after
ten years
according
to eachtoof
1 to persons
4.
The leftmost
bar, (leftmost),
representing the
initial
population
in the
model, corresponds
thethe
totalscenarios
number of obese
in the population
pyramid in Fig. 2.
Notes:
TABLE 3
Scenario
= nototal
surgery;
Scenario
2 = 3 000
operations/year
patients
over
Total expenditure
for 1
surgery,
excess costs
due to overweight
and obesity
and total costs in in
Sweden
(millionwith
SEK), BMI
and total
cost per patient
(SEK), by cost component, over ten years, according to each of the four scenarios.
40 kg/m2; Scenario 3 = 4 000 operations/year in patients with BMI over 40 kg/m2; Scenario 4
= 5 000 operations/year in patients with BMI over
38 kg/m2.
1
NumberThe
of surgical
operations
year
leftmost
bar, per
representing
Threshold for surgery
2
3
4
0 in the model,
3 000 corresponds
4 000 to the total
5 000
the initial population
number
of obese
In all patients
(million
SEK) persons in the
Total expenditure for surgery
Total excess costs due to overweight and obesity
Total costs
–
40 kg/m2
40 kg/m2
38 kg/m2
0
37 241
37 241
2 700
35 754
38 454
3 600
35 167
38 767
4 500
35 216
39 716
2 vs. 1
+2 700
–1 487
+1 213
3 vs. 1
+3 600
–2 074
+1 526
4 vs. 1
+4 500
–2 025
+2 475
2 vs. 1
90 000
–49 590
40 410
3 vs. 1
90 000
–51 840
38 160
4 vs. 1
90 000
–40 500
49 500
population pyramid in Figure 2.
Change in total expenditure for surgery
Change in total excess costs due to overweight and obesity
Change in total costs
In operated patients (SEK)
Cost of surgery, per patient
Change in healthcare costs, per patient
Total cost per operated patient
Scenario
196
S. Borg, I. Näslund, U. Persson, K. Ödegaard
DISCUSSION
Our comparison of different surgery scenarios shows
that performing 4 000 surgeries per year or less will
not reduce the number of obese in the Swedish population, but 5 000 surgeries per year will. With 4 000
surgeries per year, we saw that after ten years, all
obese male persons with BMI > 40 had been operated,
and a higher proportion of women had to be operated in order to maintain the annual rate of 4 000
surgeries. Our scenario with 5 000 surgeries per year
also used an expanded BMI range, so that anyone
with BMI > 38 kg/m2 was eligible for surgery. This
increased the number of potential candidates by some
70%, and consequently there were enough male candidates throughout the entire time frame.
Our estimates of the budget impact per annum in
Scenarios 2, 3 and 4 respectively , compared to Scenario 1 range from SEK 121 million to SEK 248 million. These estimates are in stark contrast to the
budget impact of SEK 18 000 million that we have
reported on in the introduction (8). The reason is that
the latter estimate did not count the monetary benefit
of surgeries and also used a lower BMI threshold
value for surgery, BMI > 35, had been used. Thus, the
latter estimate neglected the cost offset from reduced
morbidity, and it was also based on a much larger
patient population, both inflating the estimate.
It was recently estimated that 7 300 surgeries will
be performed in Sweden in 2010, a 50% increase compared to 2009, and it was indicated that the increase
will continue (19). This may be the case, but this rate
of surgery cannot be maintained unless the BMI
threshold is lowered as well (e.g. from today’s BMI
> 40 to BMI > 38). However, if the threshold is lowered, the cost offset seems to be declining beyond
4 000 surgeries per year. To defend such an expanded
inclusion policy, other values must be demonstrated,
such as the benefit for the patients. Our model takes
the impact of surgery on risk factors into account, in
the sense that reduced mortailty and reduced morbidity affects the costs of patients and hence the budget impact of surgery. There would also be an impact
on the patients’ quality of life (20). However, evaluating this aspect is not within the scope of the current
budget impact study. Increasing the number of surgeries within current indication (BMI > 40), requires
that a higher proportion of women is given surgery.
We believe we have captured some of the major
features of obesity and bariatric surgery in our model.
Our effort to model bariatric surgery was with the
purpose to estimate the budget impact in Sweden. To
our knowledge, two other models have been developed for this purpose. A model by Ackroyd et al was
developed and used in the settings of Germany, UK
and France (21). Their model was subsequently used
by Anselmino et al in Austria, Italy and Spain(22). A
model by Chevalier et al was developed for the
French setting (23). Although neither of these models
have been used in the Swedish setting, it may be of
interest to contrast our model to theirs, as there are
some important differences. Ackroyd’s model is limited to a cohort (closed model) of obese Type 2 Diabetes Mellitus patients (BMI > 35 kg/m2), and thus
they cannot estimate the full burden of overweight
and obesity. Chevalier’s model is based on a cohort
of 1 000 patients given surgery, so it too is a closed
model. We have an open model that has an annual
inflow of new 15 year old persons coming into age
(overweight as well as obese), corresponding to nativity. We also have an annual inflow of persons in all
ages becoming overweight, i.e. the incidence of overweight. Another central feature of our model is that
we can select different eligibility criteria (the BMI
threshold) for bariatric surgery, and the annual number of surgeries (by gender). None of the other two
models appears to have this feature. Furthermore, we
use an age-dependent BMI drift to update each person’s BMI, age, gender, and BMI specific mortality,
QALY weights etc, as explicit efforts to model patient
heterogeneity, whereas Ackroyd’s model simulates a
homogeneous cohort of obese patients, and they do
not incorporate mortality in their model. While Chevalier et al based BMI change on data from five years
of follow-up. With all these difference together, we
therefore believe we have made significant contributions to this field of modeling.
However, there are also weaknesses in our study.
The amount of data for patients with very high BMI
values are scarce, and one might have concerns that
our fitted equations for the excess costs, which grow
with the BMI squared, might have too high impact.
On the other hand, the number of persons with such
high BMI values are relatively low, so this impact is
very limited. Another uncertainty is with the dynamic
model in terms of how population strata of age, gender and degree of obesity change over time (see Fig.
2), given our submodels of mortality, patient inflow
and BMI drift. Inspection of how the population
transforms over time reveals a slow transformation
mainly due to the surgery scenario, but also due to a
growing population with increasing proportion of
overweight. Overall, however, it retains the shape in
Fig. 2. Furthermore, we believe the available Swedish
data indicate a continuing growth of the number of
overweight and obese persons. However, the peak of
the epidemic of obesity may have been reached in
nearby Finland (20). If this is true also in Sweden, the
need to increase the number of bariatric surgery operations may be exaggerated and this supports our
finding that the indication for surgery should not be
expanded. However, for such a change in the obesity
epidemic to be analysed in our model, we would
have to determine its causes and adjust relevant parameters, which could be incidence, obesity drift, and
obesity related mortality. Therefore, the incidence and
prevalence of obesity should be carefully followed
up.
CONCLUSIONS
A cost-minimization strategy for bariatric surgery in
Sweden should not expand indication, but rather increase the number of surgeries within the currently
accepted indication.
Budget impact of gastric bypass surgery
ACKNOWLEDGEMENTS
Research relating to this study was funded by an unrestricted grant from Bariatric Edge, Johnson & Johnson Nordic AB, Sollentuna, Sweden
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Received: March 15, 2011
Accepted: September 5, 2011