Download Type D personality and health-care consumption following acute

Type D personality and health-care consumption
following acute myocardial infarction
Mirela Habibovic
ANR: 490524
Masterthesis
Tilburg University
2008/2009
Dr. E.J. Martens and Dr. P.H.G.M. Soons
Abstract
Introduction: Type D personality is a synergy between negative affectivity and social inhibition. A
significant number of studies have been done on this subject and its influence on medical prognosis
and development of cardiac disease. However, no research has focused on Type D personality and
health care consumption till date. In this study we will outline the health care consumption of
acute myocardial infarction (MI) patients with a Type D personality.
Method: Post-MI cardiac patients (N=401) were included from three hospitals and completed a
number of questionnaires regarding behavioral and medical factors. These data were linked with the
data from the DBC-system (costs). The primary end point was health care consumption, as
verified by administrative health care data from the hospitals. Secondary end point was
medical outcome (emergency room visits and cardiac death and/or recurrent MI). Cox
regression analyses were performed to compare the health care costs of Type D and non Type D
patients. In addition, the costs associated with depression were assessed. The median follow-up
period was 3.7 years (SD = 1.2), and follow-up data was complete for all patients (100%).
Results: Type D personality was not a significant predictor of health care consumption. Type D
patients who were not depressed had a worse medical outcome than patients without a Type D
personality and without depression (p 0.029). No significant differences in health care consumption
were found between the depressed and non depressed patients.
Discussion: Neither Type D personality nor depression was a significant predictor of health care
consumption. Type D personality did seem to be a better predictor of medical outcome than
depression. The costs cover in the DBC-system give a global costs assessment. Future study should
focus on specifying the costs per patients and including minor procedures and more indirect costs.
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Introduction
Because today’s society does not have an infinite amount of money, it is important to
identify the best way to spend what we have. In this way the quantity and quality of life can
be maximized [1]. Outlining the costs of particular psychological constructs is not only useful
for maximizing the quality and quantity of life but could also be used in debates on policy and
financing issues, such as coverage between mental health and other types of medical care.
This way psychological constructs could get the attention they deserve and perhaps will
become an important variable in health care insurances. Not until 2008 did Dutch health
insurance cover any psychological treatment. Since 2008 a number of contacts are partly
covered by the insurance company [2]. This has been a great leap forward for psychology.
Significant number of studies have shown that mental health problems (e.g. depression) are a
common and costly problem these days and that they often go unrecognized [3]. By
objectifying the costs which go together with mental health we hope to get psychological
variables under attention and thereby help the patients to get the appropriate treatment which
will be covered by their health insurance. Although a number of studies have been done on
health care utilization and depression till date no study has been done on the health care
consumption and personality. In this study we will outline the health care consumption (HCC)
of acute myocardial infarction (MI) patients with a Type D personality.
During the last decades, many new diagnostic and treatment strategies have been
developed for cardiac patients. The prognosis and treatment of cardiac patients has improved
over the years significantly due to these developments. Unfortunately there are still some
unknown factors that appear to play a role in the development and prognosis of these patients
and probably have an influence on there health. For a significant period of time research has
focused on the psychological risk factors in cardiovascular diseases and there are a large
number of studies that report a significant influence of these factors on both the prognosis and
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development of cardiac diseases [4-12]. A well known and widely studied psychological
factor is depression which shows, among others, worse medical outcome and higher mortality
among cardiac patients [13-19]. Another focus of research has been the personality of cardiac
patients and whether this has an influence on the development and prognosis of their disease
[20-28]. Through the years Type D personality has established itself as a serious risk factor
for morbidity and mortality in patients suffering from cardiovascular disease. A great amount
of research has focused on this concept and shows that Type D personality is a stable
personality taxonomy in cardiac patients and has been associated with vulnerability to chronic
emotional distress and an increased risk for cardiac events in coronary heart disease (CHD)
patients [29-35].
People with a Type D personality show high levels of social inhibition and negative
affect. They tend to inhibit self-expression in social interaction which may lead to poor
consultation behavior. On the other hand they tend to experience more negative feelings and
to worry more about their health status [27, 35-37]. The combination of high levels of
negative affect and social inhibition can lead to emotional distress and adverse CHD
outcomes [35-37]. Within the CHD population Type D personality has shown to be positively
correlated with higher mortality rate, poor quality of life and other cardiac and emotional
complications (e.g. depression) [12, 25, 28, 29, 38-40].
Schiffer et al.[39], argued that chronic heart failure (CHF) patients with a Type D
personality show poor self-management, or self care, which is associated with adverse clinical
outcomes in CHF. It is important to notice here that personality seems to have something to
do with health care behavior and therefore probably also with HCC [41]. Many studies have
explored the effect of Type D on adverse health outcomes. The impact of Type D on health
care costs in CHD has not been explored to date. There are only a few studies that have
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examined the complicated relation between psychological variables and HCC and the overall
consensus is that psychological variables are important predictors of HCC [42-47].
Given the negative prognostic effect of Type D personality in CHD patients and
increasing prevalence of CHD it is of great importance to focus on the HCC of cardiac
patients with a Type D personality [41]. Not only because of cost-effectiveness reasons but
foremost to make sure that those patients get the appropriate treatment to maximize their
quantity and quality of life. We therefore prospectively evaluated the independent effect of
Type D at time of the hospitalization for MI on HCC.
In addition, we explored the
relationship between depression and HCC and medical outcome since depression is a well
known psychological risk factor in cardiac disease [48-50].
The transition of the Dutch Health Care System was successfully introduced January
2005 (DBC-system). The DBC-system describes the total episode of care delivered in
hospitals. Not only the inpatient care but also outpatient and daycare are reported [51-53]. All
physician payments are made through the DBC-system and the billing records include global
yet complete information on the dates and costs for medical acts carried out. By linking data
from the DBC-system with information about patient baseline characteristics collected during
a significant period of time since their first MI, including psychosocial factors and prognosis
following MI, we were able to explore the relationship between Type D personality and HCC
and the relationship with medical outcome.
In these analyses, appropriate controls for factors such as CHD severity and
depression known to affect post-MI prognosis were employed. This paper addresses the
following questions:
Health Care Consumption
1: What is the relationship between Type D personality and HCC in post-MI patients?
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1a: Do post-MI patients with Type D personality have a higher number of
hospitalizations than non Type D patients?
1b: Are post-MI patients with Type D personality more often referred to medical
psychology than non Type D patients?
Medical Outcome
2: Do post-MI patients with Type D personality have more emergency room visits than non
Type D patients?
2a: Do post-MI patients with Type D personality have higher mortality and/or
recurrent MI rates than non Type D patients?
Method
Sample
Patients hospitalized for acute MI (n = 401) age > 30, were recruited between May
2003 and May 2007 from four teaching hospitals (St. Anna Hospital, Geldrop; St. Elisabeth
Hospital, Tilburg; TweeSteden Hospital, Tilburg; and Catharina Hospital, Eindhoven) in the
Netherlands. Criteria for diagnosis of MI were troponin I levels more than twice the upper
limit, with typical ischemic symptoms (e.g. chest pain) lasting for more than 10 minutes or
ECG evidence of ST segment elevation or new pathological Q-waves. For patients without
typical angina, the day of MI onset was identified as the day during hospitalization with peak
troponin I levels >1.0 and ECG evidence of ST segment elevation or new pathological Qwaves.
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Patients with severe medical comorbidities that increased the likelihood of early death
(e.g. malignant cancer) and patients with significant cognitive impairments (e.g. dementia)
were excluded. Demographic variables (gender, age, marital status, and educational level) and
medical characteristics were obtained from the medical records. Type D personality and
depression were assessed at the time of MI. The health care consumption per patient was
estimated using the administrative health care data from the hospitals. The study was
approved by the institutional review boards of the participating hospitals, and written consent
was obtained from all study participants.
Type D personality
Type D was assessed with the 14-item type D scale (DS14), within the first week of
hospital admission for acute MI. The DS14 consists of two seven-item subscales—that is
negative affectivity and social inhibition. The 14 items are answered on a 5-point Likert scale
ranging from “false” (0) to “true”. A standardize cut-off ≥ 10 on both subscales indicates
those with a type D personality [37]. Both subscales are internally consistent en valid, with a
Cronbach’s alpha of 0.88 for the negative affectivity subscale and 0.86 for the social
inhibition subscale, and a test-retest reliability over a 3-month period of r = 0.72 and 0.82 for
the two subscales respectively [37]. Type D personality has been found stable over an 18month period in patients after acute MI [26].
Depression Assessment
Depressive symptomes were assessed using the Beck Depression Inventory (BDI)
within the first week of hospital admission for acute MI. The Beck Depression Inventory
(BDI) is a 21-item self-report measure developed to assess the presence and severity of
depressive symptoms [54]. The BDI is a widely used, valid and reliable measure of depressive
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symptomatology, with a Cronbach’s
total score
of 0.81 in non-psychiatric samples [55, 56]. A BDI
10 is indicative of at least mild to moderate symptoms of depression and has
been associated with poor prognosis in MI patients [14, 48
Patients were instructed to rate each symptom on a 0 (absent) to 3 scale with 1-3
representing incresing levels of severity. The scores range from 0 to 63. Cut-off score of 10 or
higher was used as an indicator of the presence and severity of depressieve symptoms.
Clinical characteristics
Clinical variables were obtained from the patients’ medical records and included
disease severity (cardiac history; MI, percutaneous coronary intervention, or coronary bypass
graft surgery prior to the index MI left ventricular ejection fraction (LVEF), multi vessel
disease anterior location of index MI), invasive treatment (percutaneous coronary intervention
or coronary artery bypass graft surgery), participation in cardiac rehabilitation, medication
use (beta-blockers, ACE-inhibitors, Anti-coagulants, Statins, Aspirin, Diuretics), smoking
status (self-report), body mass index (BMI), hypertension (systolic blood pressure >140,
diastolic blood pressure >90), hypercholesterolemia (total cholesterol >6.50 mmol/l), cardiac
function at the time of admission for index MI (systolic and diastolic blood pressure) and
comorbidity (diabetes mellitus, renal insufficiency, COPD and arthritis).
Health care consumption
The economic impact of outcome (health care consumption) was estimated using the
data of the direct medical care costs*, number of hospitalizations (number of hospital
readmissions for cardiac or suspected of cardiac related problems)†, referrals to Medical
*
e.g. revascularization (coronary bypass or angioplasty), catheterization, pacemaker/ ICD.
†
e.g. revascularization (coronary bypass or angioplasty), catheterization, pacemaker/ ICD.
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psychology and/or Psychiatry department. Emergency room visits (for assessment of chest
pain or suspected of cardiac related symptoms) and death/MI were combined to form one
construct: medical outcome.
Causes of readmission were classified as cardiac or noncardiac based on information
from patienst reports and hospital chart searches. All visits to the Psychiatry and Medical
psychology Department were considered psychiatric.
End points
The primary end point was HCC, as verified by administrative health care data from
the hospital. Secondary end point was medical outcome (emergency room visits and cardiac
death and/or recurrent MI; criteria for diagnosis of MI were those used for inclusion in the
study) as verified by medical records. The median follow-up period was 3.7 years (SD = 1.2),
and follow-up data was complete for all patients (100%).
Statistical analyses
All data were entered into a computerized data base and analyzed with SPSS 16.0
(SPSS Inc., Chicago, Illinois) standard software for Windows using two-tailed tests. Because
the data were skewed all analyses were based on dichotomized data.
All patients were classified in one of two groups: type-d personality (1) or non type-d
personality (0). Discrete variables were compared with the Chi-square test and are presented
as numbers and percentages and continuous variables were compared with the Student’s t-test
and are presented as means ± standard deviations.
Because of the longitudinal design of the study and the possible confounding time
aspect, univariate and multivariate Cox proportional hazard regression analyses were
performed to investigate the impact of Type D on HCC and medical outcome. A median split
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was used to indicate a low versus high number of hospitalizations, emergency room visits and
health care costs. The potentially confounding effect of biomedical and demographic factors
on outcome was tested with univariate regression analyses. The significant confounders (p
<0.05) were added as covariates to the multiple regression analyses. Age and gender were
included in all prediction models; LVEF was included to adjust for disease severity and
depression since it is a known psychosocial risk factor, ruling out the possibility that the effect
of Type D on outcome could be due to more severe cardiac disease or depression. The
Kaplan-Meier method was used to estimate the cumulative incidence of death/recurrent MI
and medical costs in Type D patients, comparing differences between groups with the logrank test. The zero time point indicates the time of hospitalization. Statistical significance was
indicated with a p-value ≤ 0.05. Hazard ratios (HR) with 95% confidence intervals (CI) are
reported.
Results
Patient characteristics
Table 1 shows the demographic and clinical characteristics of the 401 MI patients. The
prevalence of Type D personality during hospitalization for MI was 18.7% (N= 75).
Prevalence of post-MI depression was 25.7% (N= 103).
As Table 1 shows, non of the demographic and disease-related characteristics were
significantly related to Type D status. A significant difference was found between Type D and
non Type D patients in smoking status, with Type D patients being more often smokers.
Therefore, smoking was included in all prediction models.
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Table 1. Demographic and clinical characteristics*
Type-D
(n=75)
Non-type D
(n=315)
p
Demographic characteristics
Age, mean ± SD
Female gender
Partner
Educational.level: high
59.9±11.50
70 (22.2)
261 (83.7)
176 (56.8)
0.164
0.990
0.973
0.214
57.8±11.19
16 (21.3)
62 (82.7)
36 (48)
Clinical characteristics
Disease severity
Cardiac history**
11 (14.7)
52 (16.6)
0.813
LVEF %, mean ± SD
50.7±9.5
49.9±10.1
0.507
Multi vessel disease
25 (36.2)
101 (37)
0.697
Anterior MI location
29 (42)
125 (43.6)
0.925
Comorbidity
Diabetes Mellitus
8 (10.7)
44 (14)
0.571
Renal insufficiency
4 (5.4)
17 (5.4)
1.000
COPD
7 (9.5)
27 (8.7)
1.000
Arthritis
3 (4.1)
23 (7.4)
0.440
Invasive treatment***
47 (68)
207 (69)
0.805
Cardiac rehabilitation
50 (71.4)
209 (72.3)
1.000
Medication use
Beta-blockers
66 (88)
270 (86.3)
0.835
ACE-inhibitors
24 (32)
123 (39.4)
0.291
Anti-coagulants
65(86.7)
265 (84.9)
0.843
Statins
69 (92)
283 (90.4)
0.839
Aspirins
66 (88)
259 (82.7)
0.351
Diuretics
12 (16)
56 (17.9)
0.819
Smoking
39 (52)
102 (32.7)
0.003
BMI, kg/m2, mean± SD
26.3±3.8
27±3.8
0.219
Hypertension
12 (19.7)
70 (26.9)
0.315
Hypercholesterolemia
8 (13.1)
26 (10)
0.638
Cardiac function
Systolic BP, mean± SD
134.3±25
140.4±28.8
0.124
Diastolic BP, mean± SD
77.7±13.8
80.9±17.4
0.178
Estimated costs, mean± SD
7550±4144
7663±4569
0.850
Hospitalizations, mean ±SD
2.9±2.4
2.5±2.1
0.160
Referral MPS
7 (9.3)
17 (5.4)
0.192
Medical outcome
ER visits
157 (78.9)
42 (21.1)
0.338
Death and/or recurrent MI
11 (14.7)
42 (13.3)
0.908
___________________________________________________________________________
ACE= angiotensin-converting enzyme; BMI= Body mass Index; BP= blood pressure; CABG= coronary artery
bypass graft surgery; COPD= Chronic obstructive pulmonary disease; LVEF= left ventricular ejection fraction;
MI= myocardial infarction
MPS= Medical Psychology / Psychiatry Department; ER visits= Emergency room visits.
*results are presented as number (percentage) unless otherwise indicted
** Myocardial infarction, percutaneous coronary intervention, or coronary artery bypass graft surgery prior to
the index MI.
** *Invasive treatment: percutaneous coronary intervention or coronary artery bypass graft surgery.
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Univariate analyses
Type D personality was not a significant predictor of costs (p = .685), the number of
hospitalizations (p = .314), referrals to Medical Psychology / Psychiatry Department (p =
.243), emergency room visits (p = .446) and death / MI (p = .697). After combining these
variables we obtained two new constructs: medical outcome (emergency room visits and
death/MI) and health care consumption (total costs, hospitalizations and referrals to MPS).
Again, Type D was not significantly predictive of medical outcome (p = .120) and health care
consumption (p = .333).
Depression was a significant predictor of the number of hospitalizations (p = .025)
indicating that patients suffering from depression have a higher number of hospitalizations.
Also they were more often referred to Medical Psychology / Psychiatry Department (p =
.014), even after controlling for Type D personality (p = .020). Compared to non depressed,
patients suffering from depression did not have higher costs (p = .171) or more emergency
room visits (p = .158). Depression was not predictive of death / MI (p = .543), medical
outcome (p = .188) and health care consumption (p = .084).
Multivariate analyses
As shown in table 2 Type D personality nor depression were significant predictors of
Health Care Consumption.
Table 2. Multivariate predictors of Health Care Consumption*
HR
95% C.I.
Type D
1.746
0.416-7.330
Depression
2.156
0.527-8.809
Age
0.947
0.883-1.016
Sex
2.201
0.500-9.687
LVEF**
1.065
0.987-1.150
Smoking
0.619
0.140-2.728
*Total costs, hospitalizations and referrals to medical psychology/psychiatry department
**LVEF = Left ventricular ejection fraction
p
0.446
0.285
0.129
0.297
0.107
0.526
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As shown in Table 3, only age and LVEF turned out to be significant predictors of medical
outcome.
Table 3. Multivariate predictors of Medical Outcome*
HR
95% C.I.
Type D
2.176
0.839-5.648
Depression
1.122
0.450-2.799
Age
1.052
1.010-1.095
Sex
0.564
0.191-1.667
LVEF**
0.958
0.926-0.991
Smoking
0.956
0.374-2.443
*Emergency room visits and death/MI
**LVEF = Left ventricular ejection fraction
p
0.110
0.805
0.014
0.301
0.012
0.925
An interesting finding appeared in the multivariate analysis regarding
hospitalizations. Patients who reported to smoke had a significantly higher number of
hospitalizations than non smokers (p = .037).
After combining Type D personality and depression we obtained four groups. As
shown in Table 4 these groups did not differ in their health care consumption compared to the
reference group (non Type D / non depressed).
Table 4. Multivariate predictors of Health Care Consumption
HR
95% C.I.
Age
0.946
0.882-1.015
Sex
2.251
0.506-10.015
LVEF
1.069
0.987-1.159
Smoking
0.618
0.139-2.746
Group 1
1.818
0.315-10.503
Group 2
1.322
0.140-12.506
Group 3
4.046
0.658-24.891
Group 1: Type D- Depression +
Group 2: Type D + Depression –
Group 3: Type D + Depression +
Reference group: Type D – Depression -
p
0.124
0.287
0.103
0.527
0.504
0.808
0.123
Comparing the groups on medical outcome showed that the non depressed Type D
patients (group 2) had worse medical outcome than the reference group (Table 5).
Table 5. Multivariate predictors of Medical Outcome
HR
95% C.I.
Age
1.053
1.012-1.095
Sex
0.572
0.194-1.689
LVEF
0.956
0.925-0.989
P
0.012
0.312
0.008
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Smoking
Group 1
Group 2
Group 3
0.977
1.675
3.618
2.012
Group 1: Type D- Depression +
Group 2: Type D + Depression –
Group 3: Type D + Depression +
Reference group: Type D – Depression -
0.384-2.482
0.594-4.725
1.143-11.450
0.646-6.273
0.960
0.330
0.029
0.228
In these analyses, depressed Type D patients had a higher number of hospitalizations
(p = .046) and, as expected, more referrals to Medical Psychology / Psychiatry Department (p
= .014) than the reference group of non depressed non Type D.
Discussion
This is the first study that examined the relationship between Type D personality and
health care consumption. We have reported the results based on large linked administrative
health care data sets to obtain estimates of the in- and outpatient costs associated with Type D
personality in post-MI patients.
There were no significant differences between Type D and non Type D patients in
health care consumption and medical outcome. Type D patients seemed to have slightly
higher costs, a higher number of hospitalizations, more emergency room visits and worse
medical outcome but the difference was not significant. Knowing that Type D patients have
higher scores on social inhibition this could explain partially the non significance of the health
care utilization. Patients with a Type D personality may experience more complications but
are less prone to talk about it and express their worries because of the fear of rejection [27].
This could lead to a lower health care consumption and therefore only slightly higher costs
despite the complications.
With regard to depression there were no significant differences between depressed and
non depressed patients in health care consumption and medical outcome. Depressed patients
did seem to have a higher number of hospitalizations but after controlling for possible
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confounders this difference was not significant. Patients suffering from depression did have,
as expected, more referrals to Medical Psychology / Psychiatry Department than non
depressed patients.
An interesting difference appeared in the group of Type D / non depressed patients,
showing that this group had significantly worse medical outcome. This could indicate that
Type D personality, is a stronger predictor for medical outcome than depression is. Type D
personality often goes unrecognized and untreated and may perhaps therefore have more
severe medical outcome. In the univariate analyses we did find depression to be a significant
predictor of the number of hospitalizations. This means that we should not only pay attention
to Type D personality and their medical outcome, but also to depression. A significant
number of studies have focused on depression and health care utilization. Depression was
often found to be an important predictor of worse health care outcome and has been indicated
as an often occurring and costly problem within the general population [3, 57]. Although the
results in this study are not in line with former findings, we think that it is important to focus
on detection of depressive symptoms as well as Type D personality. These two constructs
often go unrecognized by medical specialists (e.g. cardiologists) and a significant number of
patients struggling with these feeling do not get the appropriate treatment.
A lot of patients are not referred to get mental help and continue living with
depression and negative feelings. To raise the awareness of patient’s depressive symptoms,
within the group of specialists working with these patients, one could think of giving
instructions to these specialists and teach them how to recognize depressive mood states and
the associated medical implications. Not only educating specialists but also patients
themselves could be useful. They could learn to name the emotions they are feeling and
perhaps ask their specialists for further treatment [58].
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Considering that recurrent hospitalizations are one of the most expensive medical
services, reduction of these costs is one of the key financial and clinical strategies in reducing
health care costs in general. Recognizing Type D personality and depression in cardiac
patients could possibly reduce their health care costs. Appropriate strategies should be
implemented to target patients that show characteristics of Type D personality e.g. social
inhibition and/or negative affect. Also engaging patients to participate to cardiac rehabilitation
could improve their awareness of negative affectivity (and /or depressive) symptoms and
possibly reduce them, with this reducing their health care consumption [58-60].
Another interesting finding is that smoking was a significant predictor of the number
of hospitalization. This means that attention must be paid to inform the patients on the adverse
outcome of smoking on health. Providing information and helping them quit smoking may
reduce the number of hospitalizations and possibly the health care consumption.
This study indicates how complications specific costs can be estimated using
administrative data. This approach may be useful in deriving costs that can be used in health
economic models in other chronic disease areas. They provide a set of health care costs of
specific disease areas that can be used for economic evaluation.
Study limitations
Finally a number of limitations of the study should be acknowledged. First, the
measure of costs was derived from the electronic data base from the hospitals. One of the
possible difficulties with deriving data from electronic database is that they could be
incomplete or inaccurate. Because of the way in which the costs are covered in the hospital
DBC-system we did not have specific individual costs per patient. The costs were not specific
enough to get an in depth analysis per patient. The costs are based on insurance claims rather
than actual use. Each code represents more than one medical act. This means that some
15
patients may have actually higher or lower costs than reported. Future studies could focus on
estimating costs per patient deriving them from the patient’s medical records. Second,
although there are a number of minor indirect costs made by post-MI patients (e.g. costs
associated with work loss), in this study we were not able to estimate them. Third, due to the
relatively small sample the continuous data remained skewed even after log transformation.
Therefore we were not able to perform analyses on these data and make estimates about actual
costs involved.
A growing body of research has demonstrated increased medical expenses in chronic
disease populations suffering from depression [44, 45, 61-63]. The results of this study are not
in line with these findings. The reason for this could be the relatively small sample size and
the costs covered in this study. As mentioned we have only looked at the costs that are based
on insurance claims and we did not attempt to estimate the minor indirect costs of these
patients. Further more, the measure of depression was based on a self-report measure, and
therefore we do not know what proportion of patients would have been classified as having a
major depressive disorder according to current psychiatric criteria.
Unfortunately there are difficulties in obtaining a complete set of data on health care
consumption because of the confidentiality and patient’s privacy. Therefore, generalizing
obtained results to other countries, in which the medical care system is more accessible or
limited, is difficult.
In conclusion
Although no significant differences were found in health care consumption related to
Type D personality and depression this study shows that non depressed patients with Type D
personality have a worse medical outcome than non Type D / non depressed patients, and that
Type D may be a stronger predictor of medical outcome than depression.
16
This study provides a set of health care costs and health care utilization that could be of
interest to economists, policymakers and health service researchers. By estimating the costs of
particular chronic disease areas, cost-effectiveness of interventions could be modeled at
reducing the rates of complications and thereby possibly reducing the general health care
consumption and costs.
17
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