variations in hospitalisation costs for acute myocardial infarction – a comparison across europe

HEALTH ECONOMICSHealth Econ. 17: S33–S45 (2008)Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1322

VARIATIONS IN HOSPITALISATION COSTS FOR ACUTEMYOCARDIAL INFARCTION – A COMPARISON

ACROSS EUROPE

OLIVER TIEMANN*

Department of Health Care Management, Berlin University of Technology, Germany

SUMMARY

The aim of this study was to determine whether between-country variations in hospital costs are larger than within-country variations and, furthermore, to explore reasons for this variability. For this purpose, we chose the primarytreatment of patients with acute myocardial infarction (AMI) as an episode of care. We obtained hospitalisationcosts and reimbursement rates from 45 hospitals in nine different EU member states (i.e. Denmark, England,France, Germany, Hungary, Italy, Netherlands, Poland, and Spain) for the year 2005. To further analyse thevariations in hospital costs, we employed a hierarchical random effects model based on treatment and hospitalcharacteristics and using purchasing power parities (PPPs) as a proxy for country-specific price levels. The between-country standard error was estimated at h2473; whereas the within-country standard error was estimated at h1242:Our regression analysis showed that percutaneous coronary intervention was associated with significantly increasedhospitals costs compared to other treatment strategies. We were able to distinguish between three groups ofcountries with different cost levels based on the number of hospitals that were able to provide these services (i.e.percutaneous transluminal coronary angioplasty (PTCA) with intracoronary stenting). Excluding Hungary,Poland, and Spain, where none of the participating hospitals were able to provide these procedures, the between-country standard error decreased to h1632; whereas the within-country standard error increased to h1416: Finally,we observed exogenous price-level effects between countries and within countries for hospitals located in urbanareas. Copyright # 2008 John Wiley & Sons, Ltd.

KEY WORDS: myocardial infarction; hospital costs; costs and cost analysis; diagnosis-related groups; regressionanalysis; European Union

INTRODUCTION

In the late twentieth century, ischaemic heart disease (IHD) was recognised as an epidemic in mostindustrialised countries and is currently a major cause of overall mortality and morbidity in adults. As aresult, IHD has received close attention from epidemiologists in many countries and is included in mostof the existing community health indicators, such as the ones used in the ‘Health for All 21’ databaseand by the European Community Health Indicators Project. The burden of disease caused by IHD isenormous, not only in terms of the number of people affected but also with regard to related costs fromsocial, third party payers’, and patients’ personal perspectives (Murray and Lopez 1997; Lambie et al.,2004).

To better understand, treat, and control IHD, cross-national data on incidence, prevalence,morbidity, and mortality, as well as on performance and quality indicators are urgently needed.However, for cross-country comparisons, reliable information on service costs and the correspondingreimbursement to providers is also crucial for analysing differences between healthcare systems.

*Correspondence to: Department of Health Care Management, Faculty of Economics and Management, Berlin University ofTechnology, Secr. EB2, Strasse des 17. Juni 145, 10623 Berlin, Germany. E-mail: [email protected]

Copyright # 2008 John Wiley & Sons, Ltd.

It is difficult to identify differences between countries if comparable information on costs and pricesacross countries is not widely available or is only available on the macro-level (e.g. standardisedinternational databases). This study is part of the third phase of the HealthBASKET (Health Benefitsand Service Costs in Europe) Project, which was funded by the European Commission, and focuses onthe feasibility of comparing costs across countries.

The aim of our study was to calculate the actual costs incurred through the provision of hospital careto patients with acute myocardial infarction (AMI). For the purpose of analysing differences in resourceconsumption and service costs, we calculated hospitalisation costs on a micro-level across nineEuropean countries (i.e. Denmark, England, France, Germany, Hungary, Italy, The Netherlands,Poland, and Spain). A further aim was to explore the reasons for variations in hospitalisation costs forindividual services and to explore whether the variations in hospitalisation costs were larger between orwithin the observed European member states. Finally, we compared costs to the prices that eachindividual hospital received from third party payers.

MEDICAL BACKGROUND

Epidemiology: IHD is the leading cause of mortality worldwide, resulting in an estimated 7.3 milliondeaths in 2002 (WHO, 2002). During that same year, 40% of all deaths in the United States could beattributed to IHD, and approximately 20% of these deaths were caused by AMI (Saleh et al., 2005). Forthe EU-25, the overall age-standardised mortality rate for IHD per 100 000 inhabitants was 105 in 2004.With a rate of 57 deaths per 100 000 population, Spain had the lowest mortality rate of the countriesincluded in the HealthBASKET project, whereas Hungary, with 233 deaths, had the highest (WHO,2007).

Terminology: As an acute and life-threatening coronary disease, myocardial infarction can be definedfrom a variety of perspectives related to clinical, electrocardiographic, biochemical, and pathologicalcharacteristics (The Joint European Society of Cardiology/American College of Cardiology Committee2000). It is generally accepted that the commonly used medical term AMI describes the necrosis(infarction) of heart muscle tissue (myocardium) resulting from a sudden reduction in coronary bloodflow (prolonged ischaemia) (Yang et al., 2006).

Pathogenesis and symptoms: Myocardial infarction is caused by the blockage of one or more coronaryarteries or their branches, usually as a consequence of clot formation in segments of a vessel altered byarteriosclerosis. Without sufficient oxygen, the affected muscle tissue undergoes irreversible necrosiswithin 3–6 h. Depending on the extent of tissue necrosis, myocardial infarction can also lead to rapiddeath. The most obvious symptom of myocardial infarction is sudden and prolonged chest pain(retrosternal), often described as a crushing or squeezing sensation accompanied by anxiety and fear ofdeath (Yang et al., 2006).

Diagnostics: In case of suspected myocardial infarction, rapid diagnosis and early risk stratificationare essential. Electrocardiographic monitoring should be performed immediately to detect life-threatening arrhythmias. The second important diagnostic tool is blood sampling, because damagedcardiomyocytes release several proteins into the circulation. Due to its high sensitivity and specificity,cardiac troponin is the preferred biomarker for detecting myocardial damage. CK-MB is the bestalternative if cardiac troponin assays are not available (Alpert et al., 2000).

Primary Therapy: The most important objective of primary therapy in patients with AMI is the rapidrestoration of coronary blood flow or reperfusion. Currently there are two main reperfusion strategies:pharmacological (thrombolytic) and mechanical (percutaneous coronary intervention; PCI). Themajority of articles published over the past ten years have focused on the effectiveness of these treatmentstrategies. Thrombolytic therapy has been shown to be effective in a large number of randomised trialsinvolving over 100 000 patients with AMI. It is most effective when administered within 3 h of symptom

O. TIEMANNS34

Copyright # 2008 John Wiley & Sons, Ltd. Health Econ. 17: S33–S45 (2008)

DOI: 10.1002/hec

onset. However, only approximately 50% of AMI patients are eligible for this therapy, and of thosetreated with thrombolytic therapy, no more than 60% achieve complete reperfusion (van de Werf andBaim, 2002).

PCI is classified as PTCA whereas intracoronary stenting (insertion of bare-metal stents or drug-eluting stents) is frequently performed in addition to coronary angioplasty to improve clinical outcomes.In 2001, 2.1 million stents were implanted worldwide or more than twice as many as in 1998. A recent,major development has been the introduction of drug-eluting stents. However, high costs and a lack ofevidence limit their use (Grech, 2003; Tung et al., 2006).

PCI is generally preferred if a catheterisation laboratory with an experienced team (i.e. includinginterventional cardiologists and skilled supporting staff) is available. The guidelines of the EuropeanSociety of Cardiology strongly recommend PCI as the preferred treatment option in cases where theprocedure can be performed within 90 minutes of the first medical contact. In contrast topharmacological therapy, approximately 95% of patients treated with PCI achieve completereperfusion. Moreover, current evidence suggests that PCI is associated with lower short-termmortality, reduced reinfarction rates, and a reduced incidence of stroke than hospital-deliveredthrombolysis. However, the longer it takes to initiate PCI treatment, the less likely that any mortalityadvantage will be seen (van de Werf et al., 2006; Yang et al., 2006; Keeley et al., 2003).

METHODS

Case description and data collection

In general, hospital cost variability can be influenced by heterogeneity in patient, treatment, andhospital characteristics, as well as by quality of care. In multinational settings, country variables such ascountry-specific price levels can also have an impact. The inclusion criteria for the present study werethus based on strictly defined patient characteristics. The population was limited to healthy malesbetween the ages of 50 and 60 and without relevant co-morbidities (e.g. diabetes, hypertension,congestive heart failure, HIV infection). Furthermore, patients were only included in the study if theypresented to an emergency department within 2 h of symptom onset with acute, prolonged chest pain.Diagnostic procedures also had to show the ECG alterations and cardiac troponin elevations typicallyassociated with a clinical presentation of myocardial infarction (Etemad and McCollam, 2005). Weexcluded bypass surgery from our analysis due to the increased length of stay (LOS) and higherhospitalisation costs associated with this procedure. Finally, the treatment (e.g. PTCA) had to becompleted without any complications (Hlatky et al., 1999).

In addition, the project partners in each participating country were required to select a sample ofhospitals that were most likely to be representative of the overall hospital setting and treatment patternsin that country. According to our selection criteria, only general acute hospitals were asked to providecost and service data for each episode of care. Teaching hospitals were explicitly excluded becauseseveral studies have shown them to have greater resource intensity and, consequently, higher costs percase (Lopez-Casanova and Saez, 1999; Rosko, 2004).

A further form of standardisation was to provide the service costs using the step-down accountingmethod as a way to distinguish more precisely between individual costs. Step-down accounting is notused in most of the participating countries, and only a few of the hospitals included in our study wereable to deliver information on resource consumption in such detail. Because of this, most of the projectpartners (e.g. Germany and England) attempted to select hospitals that could deliver data from regionalor national cost databases that are normally used to calculate diagnosis-related group (DRG) costweights. Further, costs and corresponding revenues for each episode of care (i.e. from admission todischarge) were determined from the provider’s perspective. The entire calculation was based on data

VARIATIONS IN HOSPITALISATION COSTS FOR ACUTE MYOCARDIAL INFARCTION S35


DOI: 10.1002/hec

referring to the 2005 calendar year. Productivity losses (e.g. for lost working time) were not taken intoaccount. Capital costs were also not included in the calculations of hospitalisation costs.

For the data collection process, we created a standardised reporting template to serve as acomparable basis in all countries. Filling out the template required having detailed information onresource consumption at the micro-level, such as costs per unit used, total costs for the entire stay, andproviders’ reimbursements from third party payers. The template also contained a detailed list ofoverhead costs that had to be explicitly included or excluded. The structure of the template followed themain stages of a pathway for AMI patients in an acute hospital setting. The main stages werediagnostics, primary therapy in the emergency department (accident department, cardiology, orintensive care unit depending on country and hospital), and post-treatment in a normal ward untildischarge. In addition, hospitals were required to report the allocation bases they used to allocate coststo individual cases. The hospitals also provided information on the percentage of patients receivingPTCAs with intracoronary stenting. Information on the number of inserted stents was not consistentlyreported and therefore omitted. With regard to stenting, we distinguished between the share of bare-metal stents and drug-eluting stents. At the hospital level, we collected data on the occupancy rate inpercent, the number of beds, the number of nurses, the number of physicians, and the size of theadministrative staff. In addition, the number of beds, number of physicians, and number of nurses werealso reported at the level of individual departments.

Data analysis

We conducted a regression analysis to identify factors that have an impact on hospital cost variability.Some project partners were able to provide information at the individual level (i.e. cost per case),whereas other countries delivered data from the hospital level. Therefore we aggregated the individualdata on cost per case to average costs of care per hospital. The Danish, English, Hungarian, and Polishcost values were converted into euros using average exchange rates from the year 2005. We tested totalcosts for normality using the Kolmogorov–Smirnov test and found that these were not normallydistributed at a P-value of 50:05: Previous studies have shown that healthcare expenditures generallyhave a very asymmetrical distribution. Further tests on the skewness and kurtosis of the distribution ofhospitalisation costs in our sample revealed that these were closer to a lognormal than to a normaldistribution. Thus, we used Log(Costs) as a dependent variable for the regression analysis (Dormontand Milcent, 2004).

Based on the findings of previous studies, we had to assume the presence of country-level effects (e.g.country-specific price levels) and isolate these from the effects at the hospital level using an appropriateanalytical method. With multilevel models, it is possible to take the hierarchical structure of the datainto account and provide more accurate estimates of hospital- and country-level effects (Grieve et al.,2005). In order to separate these effects, we used a two-level random intercept model (Snijders andBosker, 1999) based on the following hypothesis:

LogðCostsÞ ¼ f ðtreatment characteristics; hospital characteristics;

country-specific price levelsÞ

The set of explanatory variables representing the different treatment patterns were as follows:percentage of PTCAs performed with intracoronary stenting, share of drug-eluting stents in percent,and length of stay in days. Heterogeneity in hospital characteristics was covered by the followingvariables: number of beds per hospital, number of physicians per hospital bed, number of nurses perhospital bed, number of beds per hospital department, number of physicians per department bed, andthe occupancy rate in percent (at both the department and the hospital level). Furthermore, variationsmay also result from the well-known fact that hospitals located in urban areas often have a higher case-mix index and generally provide a higher level of care (van Bibber et al., 2006; Dimick and Finlayson,

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2006). Thus, we defined urban location as a dummy variable for hospitals located in regions with morethan 200 000 inhabitants. In addition, we used PPPs to Euro as a country-level variable (Jefferson et al.,1996; Kauf et al., 2006; Eurostat, 2007). Finally, we estimated a one-way random effect ANOVA todetermine whether variations in hospitalisation costs were larger between countries or within eachindividual country. We investigated the relationship between costs and the corresponding prices usingPearson’s correlation coefficient.

RESULTS

Descriptive statistics

For our calculations, we were able to collect data from a total of 42 hospitals. For AMI, participatingDanish researchers had access only to highly aggregated hospital data that did not allow them toexclude bypass surgery when defining the different treatment patterns. As a result, we had to exclude thehospitals from Denmark from further analysis. The number of hospitals eventually included in ourstudy varied from 2 to 13 per country. The sample characteristics for each country are given in Table I.

We observed very large variations in costs of care for AMI across and within countries. The averagetotal costs in Italy (h7450) were the highest, exceeding the average total costs in Hungary (h396) by afactor of approximately 19. On average, the total costs per case were h3766 (median h3940). Figure 1shows the minimum and maximum costs, as well as the mean costs, per country. We were able todistinguish between three groups of countries with different cost levels based on the number of hospitalsable to provide PCI procedures: (1) In Hungary, Poland, and Spain, none of the hospitals included inour study performed PCI. Hospital-level costs ranged from h309 to h2542: (2) In France, TheNetherlands, and Italy, PCI treatment was the standard intervention in all study hospitals, andthe hospitalisation costs ranged from h3720 to h9374: (3) In Germany and England, the availability ofthese procedures was dependent on the existence of appropriate infrastructure. As a result these twocountries had the highest within-country variations, with costs ranging from h1182 to h8282: Similareffects resulted when using PPP-converted hospitalisation costs (Table II). For structural reasons,approximately half of the hospitals in our sample were not able to provide PCI.

As shown in Table II, overhead costs generally accounted for a relatively large share of total costs, theaverage proportion being 26% and ranging from 12% in The Netherlands to 45% in Spain. In group 1,where none of the participating hospitals performed PCI procedures, the material costs (especially stentsin the other hospitals) were subsumed in the overhead costs. Due to its relatively small share of the totalcosts, these costs were not separately accounted, whereas this cost category accounted for 31% of totalcosts in Italy. Italian and Dutch hospitals showed the highest rate of drug-eluting stent implants.

Drug costs varied more than 50-fold, ranging from h30 in Spain to h1557 in England, and thus theproportion relative to total costs varied from 2 to 31%. The largest proportion of drug costs wasobserved in England, France, and Italy, which is due to the extensive use in these countries of the verycostly glycoprotein IIb–IIIa inhibitors to improve reperfusion outcomes after PTCA treatment (Mideiet al., 2006).

On average, the hospitals participating in our study received a reimbursement of h4462 per AMI case,which is h695 more than the mean costs of h3766 per case. Regarding the relationship between costs andprices, there is a positive linear relationship between these two variables (Pearson’s correlationcoefficient: r ¼ 0:85). However, approximately 60% of the participating hospitals received reimburse-ment above the costs incurred. For these providers, the mean level of profit was h1344; varying from h70for one Polish provider to h5000 for a Dutch hospital. Generally, whereas providers in The Netherlandsappeared to incur costs that were similar to those observed in other countries, these costs wereaccompanied by higher reimbursement rates than elsewhere. Despite this fact, it would be incorrect toassume that hospitals in The Netherlands always generate this amount of profit, because we used a



DOI: 10.1002/hec

TableI.

Sample

characteristics

England

France

Germany

The

Netherlands

Hungary

Italy

Poland

Spain

Denmark

a

Hospitalsincluded

(no.)

33

13

62

55

53

Casesincluded

(no.)

870

174

290

60

20

1788

49

438

253

Treatm

entcharacteristics

PTCA

andStenting(%

)0:33�

0:58

0:95�

0:05

0:34�

0:39

0:91�

0:15

0�

01�

00�

00�

0}

Drug-elutingStents

(%)

0:05�

0:09

0�

00:08�

0:12

0:83�

0:41

0�

00:32�

0:34

0�

00�

0}

Length

ofstay(days)

6:0�

0:08

6:0�

1:5

6:9�

2:1

5:7�

0:08

8:9�

2:1

7:0�

2:9

11:0�

3:0

8:2�

1:1

5:9�

2:7

Hospitalcharacteristics

Bedsper

hospital(no.)

776:3�

192:5

486:3�

148:5

436:5�

193:0

533:3�

250:3

608:5�

181:7

794:6�

312:9

359:4�

211:9

401:8�

275:6

585:3�

175:3

Physiciansper

hospitalbed

(no.)

0:34�

0:06

0:35�

0:04

0:22�

0:07

0:27�

0:11

0:14�

0:06

0:59�

0:03

0:27�

0:04

0:67�

0:09

0:68�

0:22

Nurses

per

hospitalbed

(no.)

1:56�

0:10

1:39�

0:22

0:56�

0:12

2:62�

0:84

0:79�

0:09

1:26�

0:14

0:76�

0:22

2:31�

1:37

2:18�

1:02

Bedsper

dept.(no.)

57:8�

032:0�

8:0

90:1�

22:9

26:0�

10:4

71:5�

12:0

34:6�

17:1

51:7�

4:6

57:8�

047:6�

22:5

Physiciansper

dept.bed

(no.)

0:23�

00:29�

0:05

0:17�

0:06

0:22�

0:06

0:09�

0:11

0:61�

0:14

0:17�

0:05

0:23�

00:45�

0:14

Countrycharacteristics

EurostatPurchasing

Power

Parities

1.0791

1.0742

1.0526

1.0519

0.6026

1.0275

0.5504

0.9077

1.3479

aForinform

ationonly;excluded

forthefurther

analysis.

O. TIEMANNS38


DOI: 10.1002/hec

simplified case that may not be representative of the majority of patients with myocardial infarction inThe Netherlands. Among the providers that incurred costs in excess of reimbursement, the mean losswas approximately h694; ranging from h68 for one German provider to h1567 for another.Reimbursement data for Spain were not available because most hospitals there have fixed budgetsand receive their payments retrospectively. The one exception is the Catalonia region, where aprospective DRG-payment system is currently in place (Sanchez-Martinez et al., 2006).

Variance analysis

Based on a one-way random effects ANOVA model, we estimated the within-country standard error tobe h1242 and the between-country standard error to be h2473:When hospitals in Hungary, Poland, andSpain, which because of completely different treatment patterns had significantly lower costs, weretaken out of the variance analysis, these estimates changed substantially. For the remaining fivecountries, the within-country standard error (h1416) nearly equalled the between-country standarderror (h1632).

Regression analysis

All of the independent variables defined above were tested within a two-level random intercept model ina general to specific approach, as part of which variables were eliminated from the model in the order ofleast significance (Hendry, 1995) (Table III). In the end, the following variables remained in themodel: percentage of PTCAs performed with intracoronary stenting, length of stay in days, urban

395.97

1025.76

1861.02

2866.36

5013.64

5916.455599.30

7450.22

592.15

1181.53

5369.53

3720.88

1415.79

2541.84

4161.15

6225.55

7616.89

308.88

2868.16

1282.55

4384.72

483.05

8282.36

9374.21

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Hungary(n=2)

Poland(n=5)

Spain(n=5)

Germany(n=13)

England(n=3)

France(n=3)

Netherlands(n=6)

Italy(n=5)

in

PCI performed in all hospitalsmixednone PCI performed in hospitals

8282.36

Figure 1. Minimum, maximum, and mean values of hospital costs per country (grouped according to number ofhospitals performing)



DOI: 10.1002/hec

TableII.Costsin

differentcost

categories

(meanvalues

per

country)

England

France

Germany

Hungary

Italy

TheNetherlands

Poland

Spain

Denmark

a

Diagnostic

Procedures

345:74h

446:79h

296:84h

70:43h

316:97h

349:71h

138:19h

349:52h

h349:52

In%

oftotalcosts

6.90

7.55

10.36

17.79

4.25

6.25

13.47

18.78

7.53

Norm

alWard/C

atheter

Lab

In%

oftotalcosts

31.40

46.45

58.63

35.34

41.48

74.66

48.14

53.67

9.76

Physician

h217:20

h614:43

h356:80

h67:95

h406:04

h316:86

h212:46

h167:67

h76:79

Nursing

h644:79

h683:56

h782:29

h71:98

h375:10

h2121:49

h210:42

h831:09

h117:67

Others

h90:51

h136:77

h50:32

bh22:18

h209:02

h70:92

bh34:54

Material

h621:65

h1313:46

h491:26

bh2286:91

h1533:33

bb

h224:17

Drugs

h1556:36

h1347:82

h164:97

h89:20

h696:36

h424:28

h189:26

h29:83

h10:78

In%

oftotalcosts

31.04

22.78

5.76

22.53

9.35

7.58

18.45

1.60

0.23

Overheads

h1537:39

h1373:62

h723:89

h96:40

h3346:66

h644:61

h204:52

h482:91

h3829:73

In%

oftotalcosts

30.66

23.22

25.25

24.35

44.92

11.51

19.94

25.95

82.48

Totalcost

h5013:64

h5916:45

h2866:36

h395:97

h7450:22

h5599:30

h1025:76

h1861:02

h4643:20

Totalcosts(P

PPadj.)

h4646:51

h5507:93

h2723:07

h657:14

h7251:03

h5322:83

h1863:61

h2050:18

h3455:00

Reimbursem

ent

h4351:00

h5731:06

h3113:96

h808:86

h7574:58

h8722:00

h932:50

cc

Profit

�h662:64

�h185:39

h247:60

h412:89

h124:36

h3122:70

�h93:26

Profitmargin

�13:22%

�3:13%

8:64%

104:27%

1:67%

55:77%

�9:09%

aForinform

ationonly;excluded

forthefurther

analysis.

bSubsumed

inoverheadcosts.

cNodata

available.

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location, and PPP. Tests for heteroscedascity and multicollinearity showed that neither was relevant tothe model. With regard to length of stay, previous studies on hospitalisation costs have argued that itshould be treated as an endogenous variable. Length of stay can be dependent on the level of costs,especially in settings with prospective payment systems, in which the national average LOS is oftenimportant for making surcharges to, and deductions from, the DRG price (Schreyogg et al., 2006).Because of this, physicians in a hospital reporting high levels of costs may be under pressure to decreaseor even increase patients’ LOS (Breyer, 1987; Evans et al., 2001). However, because the Hausman test(Hausman, 1978) did not support the endogeneity of this variable, we used LOS as an explanatoryvariable to explore the impact on resource use and corresponding costs.

DISCUSSION

Our analysis indicates that the costs for the initial hospitalisation of AMI patients vary substantiallybetween countries. Indeed, our first random effects ANOVA found that the between-country variationsin this regard were approximately twice as high as the within-country variations. The results of thesecond random effects ANOVA and the findings of our regression analysis showed that the relativelyhigh between-country variations in hospitalisation costs were due, in large part, to sizeable differences intreatment patterns, as well as to country-specific price level effects as measured by PPPs. As expected,and in accordance with other studies, the decision to provide PCI procedures is associated withsignificantly increased hospital costs. Expensive PTCA and stenting procedures are particularly strongpredictors of high hospitalisation costs (Etemad and McCollam, 2005; Evans et al., 2007; Kauf et al.,2006). These procedures require special skills and high-tech facilities, which are associated withsignificant investments in infrastructure and lead to substantial running costs. In many geographicareas, the number of patients is insufficient to justify these investments, which is why providers in ruralareas most often do not provide these interventions. Nevertheless, PCI therapy appears to be thetreatment of choice, even if the availability of laboratories that can perform the procedure variessubstantially according to the geographic region. In this regard, the United States and Japan are thecountries with the greatest capacity worldwide, although it should be noted that even in the US, only10% of hospitals are able to provide these services (Moise et al., 2003; Yang et al., 2006; Kendall, 2007).Because the use of PCI is curtailed by limited access to cardiac catheter facilities and by treatmentdelays, thrombolytic therapy still plays an important role in the treatment of AMI. In fact, it remainsthe most common reperfusion technique worldwide, because it is widely available, easily administered,and relatively inexpensive (Eagle et al., 2002). This is important information for decision makers as theydecide on a general framework and incentives for encouraging and promoting the use of PCI. Furtherresearch should therefore focus on the cost-effectiveness of increasing the number of catheterisationfacilities in rural areas.

Table III. Two-level random intercept regression model

Independent variable Coefficient SE t-Value p-Value

Treatment characteristicsPTCA with stenting 0.5249 0.1619 3.24 0:0028nnn

Length of stay 0.0725 0.0238 3.04 0:0048nnn

Hospital characteristicsUrbanity 0.2488 0.1025 2.43 0:0212nn

Country characteristicsPPPs 3.8327 0.6900 5.55 50:0001nnn

nnnThe coefficient is significant (1%), nnthe coefficient is significant (5%).



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The results of our regression analysis show that LOS was also associated with increased costs in ourdata set. Previous studies on hospitalisation costs for patients with AMI have provided some evidencethat LOS is able to explain much of the observed variation in costs (e.g. Kauf et al., 2006). In our study,the effect may be due to large differences in attitudes towards the early discharge of patients with AMI,or it may result from the widespread use of a gross-costing approach (Kaul et al., 2004). In our data set,overhead costs represent a substantial proportion of total costs. In the absence of accurate allocationbases, it is common practice to use LOS to allocate overhead costs to individual cases (Schreyogg et al.,2006).

Furthermore, exogenous price-level effects were found between countries and within countries forhospitals located in urban areas. In addition to differences in treatment characteristics, the low costs inHungary, Poland, and Spain may have been due, in part, to lower input prices (e.g. wages). Withincountries, providers located in urban areas often have increased input costs for the production process(e.g. rental or building costs). For this reason, the English system of prospective payment allows forregional variations in input prices (Epstein and Mason, 2006). A cross-country system of prospectivepayment (e.g. a Europe-wide DRG system) would need to be able to cope with these exogenousvariations. Further investigations may make it possible to adjust for these differences in input prices atthe level of individual countries, regions, or even hospitals.

According to our literature review, there are only a few studies that report on costs forAMI. Dormont and Milcent (2004) reported average total costs of h5978 per stay for AMI inFrance. Their calculations were based on 7314 cases documented in 36 French hospitals between 1994and 1997. A micro-costing approach was used to determine the hospitalisation costs for patients(female or male, aged between 40 and 100 years) who sustained an ‘uncomplicated’ myocardialinfarction and were treated with PCI procedures (PTCA with intracoronary stenting). Although thestudy by Dormont and Milcent was based on a patient population very similar to that definedin our present study, the total costs in the French study were higher. This may be due to the fact thatduring the observation period in the 1990s, PCI procedures were very innovative and rare in France andwere often provided by teaching hospitals. Approximately 5% of the patients in the study by Dormontet al. received PTCA with stenting. In contrast, HealthBASKET was conducted 8 years later, whenthese procedures had become common treatment practice in hospitals with the appropriateinfrastructure. Indeed, in the French hospitals included in our study, approximately 95% of AMIpatients received PTCA with stenting. Another reason for the higher costs observed by Dormont et al. isthat teaching hospitals are less efficient and therefore incur higher costs for comparable episodes of care(Mechanic et al., 1998; Rosko, 2004). Economies of scale might explain why the hospitalisation costs inour sample were comparable to those observed in the study by Dormont et al., at least in terms ofabsolute values.

Kauf et al. (2006) conducted a study with a sample of 5573 AMI patients admitted to 84 hospitals innine countries from November 1999 to June 2001. For most of the countries, hospital charges at valuesfrom the year 2002 were taken as a proxy instead of using actual costs. For Italy, PPP-convertedaverage total costs of h5988 (US$5662) were estimated, whereas for Germany h8113 (US$7672) and forThe Netherlands h8844 (US$8363) were determined. The comparability to the present study is limiteddue to the greater severity of AMI in the patients included by Kauf et al. In addition, hospitalisationcosts in this earlier study included the costs of bypass surgery and, at least for some countries, the costsof pacemakers, whereas drug costs were not available. The study by Kauf et al. confirmed the strongimpact of LOS and procedure used on hospital cost variations and also discussed the importance ofcountry-specific price levels. Evans et al. (2007) reported hospitalisation costs for AMI of h21 417(US$20 252) using hospital charges for 1748 AMI admissions from 10 large, private teaching hospitalsin Japan between April 2001 and June 2004. To identify risk-adjustment factors, the calculations werebased on a data set containing very heterogeneous patients (i.e. in terms of demographics and co-morbidities). The intended heterogeneity of the sample population and the inclusion of bypass surgery

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in the calculations may explain the relatively high hospital charges. Another reason is that all of theproviders in the study by Evans et al. were large teaching hospitals. In accordance with our findings,however, Evans et al. pointed out that the choice of procedures and LOS were the most influentialfactors in determining resource use and corresponding costs.

A clear limitation of our study is the small size of the hospital sample, both overall and for individualcountries. Although the study partners were advised to select hospitals that were representative of theircountry’s resource use patterns, our methodology did not include any way to control forrepresentativeness in this regard. The analyses reported here are more useful as provider benchmarksin a multinational setting than as a way to compare different healthcare systems. The external validity(i.e. generalisability) of our results would certainly increase if the data sample were larger. Examiningthe quality of services provided (e.g. in terms of comfort or alleviation of pain) was beyond the focus ofour study. In addition, there was no comparable information about patients’ quality of life after thehospital stay, about readmission, or about infections contracted during the stay. It might have beenuseful to include variables related to the quality of care as a way to evaluate the cost-effectiveness ofdifferent treatment patterns. Especially in an era of rapidly increasing social costs for medical care, thevalue of care needs to be addressed using outcome parameters. Our use of quality variables, however,was constrained by the availability of appropriate data.

CONCLUSIONS

Our study shows that the variability in hospitalisation costs for AMI patients is due, in part, toheterogeneity in treatment patterns. In addition, our regression analysis indicated that price-level effectshave an impact on the variations between countries and within countries (e.g. urban vs rural areas). Thisis important information for decision makers responsible for developing reimbursement schemes. InDRG systems, especially if the use of a specific service package (e.g. PCI) is supposed to be encouraged,reimbursement will also need to account for regional differences. Thus, despite differences in treatmentpatterns, reimbursement rates have to be adjusted for different price levels (e.g. wages) and structuraldifferences (e.g. teaching status, level of care) to achieve adequate reimbursement with per casepayments. Structural components may play an even more important role than the heterogeneity oftreatment patterns in cost variations within an episode of care. If this is true, then many European DRGsystems may be heading in the wrong direction by concentrating on further developing the medicalclassification of DRGs. Because of this, future research should identify regional and structural factorsfor adjusting hospitalisation costs. Adjusting for these factors would likely enable cross-national,European comparisons of healthcare providers, thus increasing efficiency and paving the way to aEurope-wide DRG system.

ACKNOWLEDGEMENTS

The results presented in this article are based on the project ‘Health Benefits and Service Costs inEurope – Health BASKET’, which was funded by the European Commission within the SixthFramework Research Programme (grant no. SP21-CT-2004-501588).

CONFLICT OF INTEREST

No conflicts of interest declared.



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variations in hospitalisation costs for acute myocardial infarction – a comparison across europe

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