Cost-of-illness studies of atrial fibrillation: methodological considerations.

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Review

Cost-of-illness studies of atrial fibrillation: methodological considerations Expert Rev. Pharmacoecon. Outcomes Res. 14(5), 661–684 (2014)

Christian Becker Institute of Health Economics and Health Care Management, Helmholtz Zentrum Mu¨nchen – German Research Center for Environmental Health, Neuherberg, Germany and Munich Center of Health Sciences, Ludwig-Maximilians-Universita¨t Mu¨nchen, Mu¨nchen, Germany Tel.: +49 893 187 3943 Fax: +49 893 187 3375 christian.becker@helmholtz-muenchen. de

Atrial fibrillation (AF) is the most common heart rhythm arrhythmia, which has considerable economic consequences. This study aims to identify the current cost-of-illness estimates of AF; a focus was put on describing the studies’ methodology. A literature review was conducted. Twenty-eight cost-of-illness studies were identified. Cost-of-illness estimates exist for health insurance members, hospital and primary care populations. In addition, the cost of stroke in AF patients and the costs of post-operative AF were calculated. The methods used were heterogeneous, mostly studies calculated excess costs. The identified annual excess costs varied, even among studies from the USA (~US$1900 to ~US$19,000). While pointing toward considerable costs, the cost-of-illness studies’ relevance could be improved by focusing on subpopulations and treatment mixes. As possible starting points for subsequent economic studies, the methodology of cost-of-illness studies should be taken into account using methods, allowing stakeholders to find suitable studies and validate estimates. KEYWORDS: atrial fibrillation • cost-of-illness • decision support technique • health economics • review

Atrial fibrillation (AF) is one of the most common heart arrhythmias. Currently, it is estimated that approximately 5.2 million people in the USA and 8.8 million in the European Union have AF [1,2]. There are also reports from other regions, which indicate that AF is a health problem worldwide [3,4]. The prevalence of this disease has been found to be strongly increasing with age: in Germany, it was estimated to be about 2% in the general population and almost 18% in men aged 85–89 [5]. There is an overall lifetime risk of developing AF at the age of 55 years of about 24% in men and 22% in women [6]. Being associated with an up to fivefold increased risk of stroke, AF is recognized to be a major source of mortality; likewise, it has been reported to be among the most common reasons for hospital admission [7–9]. Over the last years, the economic burden of AF to healthcare payers and to society has shifted the focus of health economic analysis, especially in light of aging populations, an increasing prevalence of risk factors and improvements in the treatment of related heart disease, which all contribute to a further increasing prevalence of AF over the near

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10.1586/14737167.2014.940904

future [1,10]. In this context, cost-of-illness studies aim to provide decision-makers with current estimates of the disease’s economic burden in monetary terms and to identify the major drivers of these costs. Moreover, they are used to direct attention toward a health problem in order to promote research in this area and are thus considered the first step toward economic evaluation [11]. As healthcare system, population characteristics and a broad range of methodological choices influence cost-of-illness estimates, these may not be applicable to particular decision problems; hence, careful interpretation of the results is necessary [11–14]. In order to make best use of cost-of-illness estimates, decision-makers should know the existing cost estimates and be able to judge their applicability to a particular decision problem. Understanding the study methodology, furthermore, enables stakeholders to approximately assess the comparability of studies, which is important for bringing together results from different studies or for validation. Therefore, it is the aim of this study to provide a systematic overview of the current

2014 Informa UK Ltd

ISSN 1473-7167

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cost-of-illness estimates of AF, to examine the studies’ methodology as well as their overall comparability.


Methods

In order to identify the recent cost-of-illness studies on AF, a literature review was conducted. The review was conducted independently by two reviewers on 30 May 2014 using the PubMed/Medline, EMBASE and EconLit databases. The search terms were: ‘costs’, ‘expenditure’, ‘economic’ and ‘atrial fibrillation’ as free text terms and, where possible, as subject headings. Studies were included if these included an original calculation of the cost-of-illness of AF over a defined period of time and were written in English or German. Because cost-ofillness estimates are based on medical guidelines and health technology, current at the time they were conducted, and it was aimed to provide up-to-date information on the current cost-of-illness estimates, studies were excluded if they were published before 2003. Aside from the methodological characteristics, the studies’ cost-of-illness estimates were extracted. In order to account for inflation, these were inflated to the price level of 2012 using the national inflation rates according to the Organisation for Economic Co-operation and Development, the most recent year with complete data. Next, to account for different currencies, these values were converted to the US-dollar using purchasing power parities. In order to examine the studies’ methodology, 12 study characteristics were selected based on previous cost-of-illness literature [11–14]. First, general characteristics, such as country of origin, study period, time horizon, type of data analyzed (administrative data, registry data or survey data) and level of aggregation (top-down or bottom-up) were examined. Furthermore, disease definition and population characteristics were extracted. Moreover, the epidemiological approach (prevalencebased or incidence-based), direction of data collection (prospective or retrospective), the method of cost-of-illness calculation (AF-related costs, excess costs or excess costs pre- vs postindex), the included cost components and the unit valuation were documented. In order to provide an overview of the studies’ overall comparability, the study characteristics were also provided in a pairwise fashion: two-way comparisons were conducted among the identified studies with regard to the characteristics ‘country of origin’, ‘study period’, ‘epidemiological approach’, ‘time horizon costs are estimated for’, ‘type of data analyzed’, ‘the studies’ ‘level of aggregation’, study ‘population characteristics’, ‘direction of recording cost data’ and the ‘method of cost-of-illness calculation’. In order to account for the fact that the perspective of cost calculation is not always stated, this characteristic was broken down into included cost components and unit valuation. If studies provided cost-of-illness estimates based on different characteristics, such as different countries, in the two-way comparison, the matching characteristic was selected where applicable. Given an available costof-illness study, this approach was assumed to enable 662

researchers and decision-makers to find studies with common characteristics, which can serve as starting point for more detailed analyses. Although the criteria used in this comparison were assumed to be of equal importance, a high number of shared characteristics can be assumed to also indicate a good overall comparability. Moreover, the comparability of studies from the same country, analyzing the same data type and using the same method of calculating costs were highlighted as these were assumed to be ‘more comparable’. Building upon previously published checklists, the studies’ quality of reporting was assessed. Items relating to the quality of reporting from the checklists by Larg and Moss and Molinier et al. were combined [11,15,16]. Results Literature review

The results of the literature review are shown in FIGURE 1. Overall, the review resulted in 1160 results from PubMed/Medline, 932 results from EMBASE and 9 results from the EconLit database. After initial screening of titles and abstracts, 808 results from the PubMed database, 998 results from the EMBASE database and 5 results from the EconLit database were not eligible for inclusion and were thus excluded. Following an assessment of the remaining results, 99 articles were excluded because they compared costs that were not related to AF, 37 articles were based on published cost-of-illness estimates and 25 articles were excluded because they did not report costs or did not report over a defined period of time. After removing duplicates, overall 28 articles met the inclusion criteria and were included in this review. General characteristics of the cost-of-illness studies

Details on the included cost-of-illness studies are provided in TABLE 1. The studies analyzed data between 1995 and 2011 and were published between 2004 and 2014. All of the identified studies were conducted in North American and European settings. There were 13 studies from the USA and 1 from Canada. The remaining 14 studies were from different European settings: 4 from Germany, 2 from Sweden, 3 from the UK and 1 from France. In one study, it was unclear in which European setting it was conducted. One study was conducted both in Germany and Sweden, and two were based on data from five European countries, namely, Greece, Italy, Poland, Spain and The Netherlands. Twenty-three studies were based on individual-level cost data and can therefore be classified as bottom-up [14]. The remaining five studies may be classified as top-down, because these were based on more aggregated cost data [17–22]. Administrative data were the most common type of data analyzed. Ten studies were based on claims data from health insurance providers [23–33]: Medicare [23–25], managed care organizations (sponsored both by Medicare and commercial insurance providers) [27,30,31,33], employer-sponsored health plans [28,29] and a German sickness fund [26]. In addition, one study used billing data from a hospital [34], and two studies used data from registries [35,36]. Three Expert Rev. Pharmacoecon. Outcomes Res. 14(5), (2014)


Cost-of-illness studies of atrial fibrillation

studies analyzed aggregated healthcare utilization data from national accounts [18,19,21], two of these focusing on national hospital utilization [18,19]. Two studies combined multiple sources of data to estimate national level healthcare utilization [20,22]. Nine studies were based on surveys that were conducted in primary care settings [37–41], in the hospital setting [42,43] or in both [44,45]. The studies’ target populations were mostly aged well above 65, especially in the studies that included Medicare members. In contrast, two studies analyzed beneficiaries of employersponsored health plans who were, on average, aged between 48.9 and 55.3 [28,29]. The managed care organizations members targeted by Kim et al. had an average age of 62.7 and 64 years [31,32]. Few studies stated inclusion criteria with regard to pre-existing disease, most notably, three studies included stroke patients only [33,36,42]. In addition, Amin et al. [23] and Reinhold et al. [26] focused on AF patients with pre-existing cardiovascular risk factors.

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Database search PubMed/ Medline

EMBASE

1160 records 932 records screened screened

EconLit 9 records screened 1811 records excluded

216 full texts assessed for eligibility

55 studies included in review

161 records excluded • Costs not associated with AF: 99 • No original cost calculation: 37 • No costs reported/ no time reference: 25

27 duplicates removed

Definition of disease

In studies of administrative data, the study population was established by screening the database for people with AF-related claims or medical encounters. In four studies, claims could also be related to atrial flutter [18,20,23,24]. Owing to the often intermittent nature of AF, multiple years need to be screened to identify a large number of patients. Whereas most studies chose a sensitive approach and considered primary or secondary diagnoses of AF during the indexing period [18,21–23,32], three studies, however, considered primary diagnoses codes only [25,26,41]. As administrative data are subject to coding errors, there were approaches of verifying the diagnosis of AF. Several studies required a single diagnosis of AF at an inpatient setting or more than one diagnosis at an outpatient setting [23,27,31,33], and two required at least two AF-related claims at any clinical setting [28,30]. Boccuzzi et al. studied chronic AF, which was defined as at least two AF-related claims within 45 days [30]. The studies that were based on surveys usually included AF patients who were undergoing treatment in the study setting. The presence of AF was established using ECG records [34–36,38,39,43–45] or Holter records [44,45]. One study did not provide further details on the definition of disease [37]. Several studies further narrowed down the definition of disease. Specifically, AF was excluded if diagnosed shortly after cardiac surgery [25,30,32,35], or if linked to valve disease [21,30,38]. Two studies focused on post-operative AF [34,43]. Moreover, three studies excluded ‘transient’ AF, indicated by the diagnosis of hyperthyroidism and related prescription medication claims prior to index [23,27,32]. Epidemiological approach

Most of the identified studies estimated the cost-of-illness according to a prevalence-based approach, which includes cases of AF that were prevalent during a defined period of time [14]. The top-down studies were based on the overall healthcare utilization due to AF that was incurred in one informahealthcare.com

28 studies finally included in review

Figure 1. Results of the literature review.

given year [18–22]. Seven of the bottom-up analyses of administrative data included people with AF-related healthcare utilization during identification periods [23,25–29,31,32], which ranged between 6 months [25] and 6.25 years [29]. These studies usually took into account the costs incurred by each individual in a forward-looking direction, starting from the index date until 1 year post-index. Studies that were based on surveys included people with AF who were recruited during 1 year [37,39,42–45], 9 months [40] or 6 months [38,41]. Where the individual resource use was collected through retrospective questionnaires, this was done either at baseline visit [39,40] or at follow-ups [37,38,44–46]. One study obtained clinical records on the most recent 3 years of management prior to index [41]. Five of the bottom-up studies used incidence-based approaches, which include cases newly identified during in a defined period of time [14]. Boccuzzi et al. focused on incident cases of chronic AF [30]. The studies by Lee et al. and Kim et al., and one of the subgroups studied by Kassianos et al., included patients with new AF [24,32]. Bhave et al. and Rostagno et al. studied new cases of post-operative AF after major non-cardiac surgery [34] or cardiac surgery [43]. In addition, Sussman et al. analyzed incident cases of stroke [33]. While Boccuzzi et al., Lee et al. and Kim et al. identified new cases of AF based on event-free periods of 1 year prior to index, Bhave et al. used present on admission codes [34]. Two incidence-based studies analyzed registry data: Ghatnekar and Glader, who included first-ever stroke patients and prospectively calculated costs for up to 3 years after inception [36] and Reynolds et al., who included patients after their first 663

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Table 1. Characteristics of the identified cost-of-illness studies on atrial fibrillation. Study (year)

Country

Aggregation

Epidemiological approach

Data source

Population

Amin et al. (2011)

USA

Bottom-up

Prevalence-based

Administrative data

58,555 Medicare members, 58,555 matched controls (m age: 80 years)

Boccuzzi et al. (2009)

USA

Bottom-up

Incidence-based

Administrative data

3891 MCO members (m age: 67.5 years)

Bhave et al. (2012)

USA

Bottom-up

Incidence based

Administrative data

Resource use based on 370,447 hospital patients with post-operative AF (m age: 74.6 years [AF], 62.4 [non-AF])

Bru¨ggenju¨rgen et al. (2007)

Germany

Bottom-up

Prevalence-based

Survey

367 acute stroke hospital patients, 71 with AF (m age: 74 [AF], 64 [non-AF])

Coyne et al. (2006)

USA

Top-down

Prevalence-based

Administrative data

Administrative data from three national databases

Ericson et al. (2011)

Sweden

Top-down

Prevalence-based

Published estimates

Based on published sources from Sweden

Ghatnekar and Glader (2008)

Sweden

Bottom-up

Incidence-based

Registry data

6611 stroke patients from registry, 1619 with AF (m age 80 [AF], 73 [non-AF])

Holstenson et al. (2011)

Greece; Italy; Poland; Spain; The Netherlands

Bottom-up

Prevalence-based

Survey

671 inpatients and outpatients with AF (m age 69)

Jo¨nsson et al. (2010)

Sweden; Germany

Bottom-up

Prevalence-based

Survey

922 primary/specialist care patients with AF (Sweden: 384 [m age 67], Germany: 538 [m age 63])

Kassianos et al. (2014)

UK

Bottom-up

Prevalence-based/ incidence-based

Survey

1245 primary care patients: (ABL: 2% IP, 2.2% MP; PI: 0.6% IP, 1.4% MP; CDV: 2.6% IP, 5.9% MP)

Keech et al. (2012)

UK

Top-down

Prevalence-based

Administrative data

National hospitalization in Scotland

Kim et al. (2009)

USA

Bottom-up

Incidence-based

Administrative data

3549 managed care patients (employer sponsored health plan or Medicare) with AF (m age 62.7)

ABL: Catheter ablation; AF: Atrial fibrillation; CDV: Cardioversion; E: Spain; EPS: Electrophysiological study; GR: Greece; I: Italy; IP: Initiation phase; m: Mean; MCO:

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Review

Inclusion criteria

Direction of cost calculation

COI method

Study period

Time horizon

Ref.

Age ‡75 (without additional risk factors), or ‡70 with ‡1 additional risk factors; primary/secondary diagnosis of AF/atrial flutter (‡1 inpatient claim or ‡ non-diagnostic outpatient medical service claims); continuous coverage 12 month before and after index claim; no transient AF/atrial flutter, no acute myocarditis or ESRD, no CHF in pre-index period

Prospective

Excess costs

2006–2009

1 year

[23]

Age ‡45 years; ‡18 months MCO membership (at least 6 months after first AF claim); presence of chronic non-valvular AF (‡2 claims for AF at least 45 days apart; no cardioversion, cardiac ablation or warfarin use during identification period, no valve-related diagnoses; no claim for cardiac ablation or cardioversion, unless there was a claim for AF at least 45 days later)

Prospective

Excess costs (pre- vs postindex)

2000–2002

6–24 months

[30]

Age ‡18; major non-cardiac surgery; post-operative AF: secondary diagnosis of AF and charge indicating need for therapy; no chronic AF

Prospective

Excess costs

2008

1 calendar year

[34]

Presentation £7 days after onset of symptoms of acute stroke; no transfer or death within 1 day after admission; no inhospital stroke; German-speaking; ECG-confirmed AF

Retrospective

Excess costs

2000–2002

1 year

[42]

Inpatients: primary or secondary diagnosis of AF; outpatients: diagnosis of AF, prescription of AF-related medication or AFrelated procedure; both groups: no valve-related diagnosis

Retrospective

AF-related costs, excess costs (AF as secondary diagnosis)

2001

1 year

[21]

Primary or secondary diagnosis of AF

Retrospective

AF-related costs

2007

1 year

[22]

First-ever stroke patients without subarachnoid hemorrhage; AF group: available ECG records, ECG-confirmed AF or atrial flutter

Prospective

Excess costs (stroke-related)

2001–2003

3 years

[36]

Age ‡18; ECG- or Holter-confirmed AF in last 12 months

Retrospective

CVD-related costs

2003

1 year

[45]

Age ‡18; diagnosis of AF

Retrospective

AF-related costs

2005–2006

1 year

[40]

Age ‡18; diagnosis of AF or atrial flutter £12 weeks before data collection; no secondary AF

Prospective

AF-related costs

2010

3 years

[47]

Age ‡55; primary or secondary diagnosis of AF

Retrospective

AF-related costs

2004–2008

4 calendar years

[19]

Age ‡18; new diagnosis of AF (no diagnosis of AF or use of rate-control or rhythm. Control therapy during 12 months before index); continuous enrollment 12 months before and after index; no comorbid hyper-thyroidism (transient AF) or single claim for AF 30 days after cardiac surgery (isolated AF), ‡1 claim for rhythm control therapy £6 months after index

Prospective

AF-related costs

2001–2007

1 year

[31]

Managed care organizations; MP: Maintenance phase; NL: The Netherlands; n.r.: Not reported; PI: Pacemaker implant; PL: Poland.


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Table 1. Characteristics of the identified cost-of-illness studies on atrial fibrillation (cont.). Study (year)

Country

Aggregation


Data source

Population

Kim et al. (2009)

USA

Bottom-up

Prevalence-based

Administrative data

35,255 health plan members with AF and 20,571 controls (m age 64)

Kim et al. (2011)

USA

Bottom-up

Prevalence-based

Administrative data

89,066 employer sponsored health plan/Medicare members with AF; 89,066 matched controls (m age: 71.3 [AF], 71.5 [non-AF])

Lee et al. (2008)

USA

Bottom-up

Incidence-based

Administrative data

55,260 Medicare (AF), 55,260 matched controls (PI: 1.6% [AF])

Le Heuzey et al. (2004)

France

Bottom-up

Prevalence-based

Survey

671 primary care patients with AF (m age 69)

McBride et al. (2009)

Germany

Bottom-up

Prevalence-based

Survey

361 primary care patients with AF (m age 71)

O’Reilly et al. (2013)

Canada

Top-down

Prevalence-based

Administrative data

Aggregate utilization of hospital and ambulatory care in Canada

Pelletier et al. (2005)

USA

Bottom-up

Prevalence-based

Administrative data

59,648 Medicare beneficiaries with AF (m age: 78.1)

Reinhold et al. (2012)

Germany

Bottom-up

Prevalence-based

Survey

3667 primary care patients with AF (m age: 72.1 years) (CDV 11%, ABL 3%, PI: 10%)


Germany

Bottom-up

Prevalence-based

Administrative data

14,798 health insurance members (m age: 72.2)

Reynolds et al. (2007)

USA

Bottom-up

Incidence-based

Registry data

973 patients from registry (m age: ~65 years) (PI: 8.8%, CDV: 5.9%)

Ringborg et al. (2008)

Greece; Italy; Poland; Spain; The Netherlands

Bottom-up

Prevalence-based

Survey

In- and outpatients with AF: 323 (GR); 843 (I); 267 (PL); 848 (E); 714 (NL); (m age: 66–70 years) (EPS: 3% [GR], 2% [I], 5% [PL], 2% [E], 3% [NL]; CDV: 13% [GR], 41% [I], 33% [PL], 16% [E], 29% [NL]; ABL: 3% [GR], 1% [I], 4% [PL], 7% [E], 3% [NL]; PI: 4% [GR], 6% [I], 9% [PL], 5% [E], 5% [NL])

Rohrbacker et al. (2010)

USA

Bottom-up

Prevalence-based

Administrative data

1403 employees with AF and 323,333 controls (m age: 48.9 [AF], 39.7 [non-AF])

Rostagno et al. (2010)

n.r.

Bottom-up

Prevalence-based

Survey

215 patients who developed AF after cardiac surgery (m age: 71)

Stewart et al. (2004)

UK

Top-down

Prevalence-based

Published estimates

Based on published sources from UK


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Inclusion criteria


COI method

Study period

Time horizon

Ref.

Age ‡20; ‡1 inpatient or ‡2 outpatient diagnoses of AF

Prospective

AF-related costs/ excess costs

2005

1 year

[32]

Age ‡20; ‡1 inpatient or ‡2 outpatient diagnoses of AF; no enrollment £12 months before index date; no patients with transient AF; controls: no anti-arrhythmic medication, AF or atrial flutter

Prospective

Excess costs

2004–2006

1 year

[27]

Medicare members; primary or secondary diagnosis of AF

Prospective

Excess costs

2002–2004

1 year

[24]

No explicit inclusion criteria

Retrospective

AF-related costs

n.r.

1 year

[37]

Age ‡18; ECG-confirmed AF for last 3 months; ability to fill out questionnaire; no cardiac valve disease or pregnancy; no participation in coagulation study

Retrospective

AF-related costs

2003–2004

1 year

[38]


Retrospective

AF-related costs

2007–2008

1 year

[18]

Age ‡65; principal diagnosis of AF; continuous enrollment in Medicare Part A fee-for-service; presence of only one AF diagnosis in physician file, but none in hospital file; no cardiac surgery or hyper-thyroidism in two quarters prior to index AF diagnosis and no subsequent AF diagnoses

Prospective

AF-related costs

1998–1999

1 year

[25]

Age ‡18; ECG-confirmed AF

Retrospective

AF-related costs

2009

1 year

[39]

Being continuously insured; pre-existing cardiovascular disease; primary diagnosis of AF at inpatient setting

Prospective

Excess costs (pre- vs postindex)

2004–2005

1 year

[26]

ECG-confirmed AF not within 7 days of heart surgery

Retrospective

AF-related costs

1997–2000

1 year

[35]

Age ‡18; ECG- or Holter-confirmed AF in past 12 months

Retrospective

AF-related costs

2004–2005

1 year

[44]

AF-related claim (ICD 427.31) (no such claim for control group); continuous enrolment in health plan for ‡1 year after index

Prospective

Excess costs

2001–2008

1 year

[29]

No chronic AF or maze procedure

Prospective

AF-related costs

2007

1 calendar year

[43]


Retrospective

AF-related costs

1995–1996

1 year

[20]



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Table 1. Characteristics of the identified cost-of-illness studies on atrial fibrillation (cont.). Study (year)

Country

Aggregation


Data source

Population

Sussman et al. (2013)

USA

Bottom-up

Incidence-based

Administrative data

3047 employer-sponsored health plan members (commercial and Medicare supplemental) with stroke and AF and 23,557 control subjects (m age: 55.3 [AF], 51.9 [non-AF])

Wu et al. (2005)

USA

Bottom-up

Prevalence-based

Administrative data

3944 health plan members with AF and 3944 matched control subjects (m age: 55.3 [AF], 51.9 [non-AF])


ECG-confirmed episode of AF and calculated costs based on multiple follow-up interviews [35]. Time horizon

Most studies focused on the period of 1 year. The five topdown studies each calculated the costs of AF in one particular calendar year [18–22]. All of the prevalence-based bottom-up studies and the incidence-based study by Lee et al. calculated the cost-of-illness for the period of 1 year after index [24]. Kassianos et al. calculated 1-year costs following an initiation phase of 3 months, costs incurred during this period were reported separately [47]. Furthermore, Bhave et al. and Rostagno et al. calculated costs that were incurred in the calendar year of index [34,43]. Three studies used longer time horizons: Boccuzzi et al. had a follow-up period of between 6 and 24 months [30], the study by Ghatnekar and Glader had a time horizon of 3 years [36], and Reynolds et al. followed patients over a period of up to 30 months [35]. Whereas Ghatnekar and Glader reported discounting at 3%, Reynolds et al. did not report using discounting [35,36]. Method of cost-of-illness calculation

Seventeen of the identified studies calculated the cost-of-illness as sum of those cost items that were assumed to be related to AF, making this the most commonly used method of cost calculation. This approach requires a catalog of selected resources that were assumed to be related to AF or another method of determining the relevant utilization. Where administrative data were analyzed, utilization was linked to AF based on any diagnosis code of AF [18,19,21,31–33] or based on primary diagnosis codes only [25,41]. In surveys or analyses of registry data, AFrelated utilization was identified by physicians [38–40], or reported by patients [35,44,45]. In one study it was not clear how AF-related utilization was identified [37]. Two of the top-down studies used estimates on AF-related utilization from published sources [20,22]. Nine studies used excess-cost approaches, which include all healthcare costs regardless of being related to AF or not. These 668

calculate the costs due to AF as the cost difference of people with AF and a control group [12]. Sussman et al. calculated those excess costs of AF that were related to stroke only [33]. In two studies, excess costs were calculated by comparing the same individuals both before and after index date, assuming the healthcare costs prior to the index admission not to be related to AF [26,30]. Four studies used a matched control design to allow for comparable study groups [23,24,27,28]. Three of these also used regression models to adjust for confounders [23,24,28]. Three excess-cost studies used regression adjustment without matching study groups [29,34,42]. Two studies used mixed costing approaches. Kim et al. summed the disease-related costs for patients who had a primary discharge diagnosis of AF or who were outpatient managed. Incremental costs were calculated for patients with secondary inpatient diagnoses of AF [31]. A similar approach was pursued by Coyne et al., who focused on cases rather than individual patients [21]. Perspective of cost calculation

The studies’ perspective and included cost components are displayed in TABLE 2. Overall, 17 studies stated their costing perspective. Eight studies took the perspective of social insurance carriers [19,20,24–26,37,39,41] and four took the perspective of other third-party payers [21,23,33,38], two from an employer’s perspective [28,29]. Further perspectives were of the USA and of the Canadian hospital sector [21]. Two studies calculated costs from two alternative perspectives [28,37]. TABLE 2 also shows the cost components that were included in the cost calculations. In 18 studies, the components of hospital inpatient, hospital outpatient or specialist services, general practitioner services and pharmaceuticals were included. In addition, three studies included rehabilitation [20,39,42], four included long-term care [20,24,41,42] and another three included transportation costs [22,38,42]. Productivity losses were included in nine studies [22,26,28,29,37,40,42,44,45]. Four studies included the costs of inpatient hospital care only [19,34,36,43] and O’Reilly et al. included in- and outpatient hospital care [18]. Expert Rev. Pharmacoecon. Outcomes Res. 14(5), (2014)



Review

Inclusion criteria


COI method

Study period

Time horizon

Ref.

Age ‡ 18; ‡1 primary inpatient diagnosis of stroke or transient ischemic attack or ‡1 ED diagnosis of transient ischemic attack (survivors of index event included); continuous eligibility and no stroke for ‡12 months prior to index event; AF group: ‡1 inpatient or ‡2 outpatient claims with AF diagnosis £12 months before index and £3 months after index

Prospective

Excess strokerelated costs

2005–2011

1 year

[33]

Age £65; ‡2 medical visits with diagnosis of AF

Prospective

Excess costs

1999–2002

1 year

[28]


The studies either had access to cost data from administrative sources, or multiplied resource utilization with unit costs. Nine studies used the actual reimbursed prices [19,23–25,28,29,31–33]. Two studies were mainly based on healthcare payer reimbursement: Reinhold et al., where unit costs were used to value physician visits [26] and Kim et al., where average payments were used for individuals under capitated arrangements and patient copayments were added [27]. Three studies calculated costs using hospital charges [34,36,43]. The remaining studies were based on unit costs. Six of these priced hospital stays using average costs per day [35,37,40,42,44,45] and one used average costs per stay [27]. Six studies were based on average reimbursements for diagnosis-related groups [22,30,35,38] or other measures of resource intensity [18,39], one used adjusted provider charges [21]. Pharmaceuticals were mostly priced using wholesale prices [21,35,38–40,42,44,45]. Two studies calculated the reimbursed share of the wholesale price [30,37] and one study used net prices [20]. Physician consultations and outpatient procedures were valued at the average prices per visit [18,21,35,37,38,40–42,44,45]. In addition, Boccuzzi et al. used the resource-based relative value scale [30]. Where included, inpatient rehabilitation was valued at the average costs [38,39,42]. Productivity losses were priced at the average salary per day in six studies [22,37,40,42,44,45]. Estimated costs

In order to attain a higher degree of comparability, the cost estimates from the included studies are displayed in multiple tables (TABLES 3,4,5). The results of the top-down cost-of-illness studies are listed in TABLE 3. Estimates from the bottom-up costof-illness studies are separately provided by the method of cost calculation: TABLE 4 shows the estimates from excess-cost studies and TABLE 5 shows the estimates from studies calculating diseaserelated costs. Moreover, within each table studies are grouped by country. Nineteen of the studies disaggregated the total cost-of-illness estimate into contributing cost components to identify the main driver of costs. Although the particular percentage varied, hospital costs were, with few exceptions, identified as the main informahealthcare.com

cost driver. In addition, Kassianos et al. found that hospitalization costs were particularly high during the first 12 weeks after AF was first diagnosed [41]. The top-down study by Ericson et al. identified ‘direct cost of complication’ as the largest cost component, which include hospitalizations for comorbidities [22]. McBride et al. identified a subsample of patients under a rate control treatment regimen where pharmaceutical costs were the main cost driver [38]. In the Greek and Dutch samples of the study by Ringborg et al., outpatient care was the largest component [44]. Most excess-cost studies found positive excess costs for all reported components, except for Kim et al., who found patients with AF to incur less pharmaceutical cost, and Bru¨ggenju¨rgen et al., who found fewer productivity losses in a subsample of patients [27,42]. Several of the studies calculated costs also for subgroups of AF patients. Reynolds et al. selected four groups according to the number of AF recurrences or status of permanent AF. Costs were found to be lowest for people with permanent AF and were increasing with the number of recurrences [35]. Reinhold et al. found costs to be highest in people with paroxysmal AF and lowest in patients with permanent AF [39]. Similarly, Jo¨nsson et al. also found the total cost to be lowest in patients with permanent AF [40]. In the study by Kassianos et al., costs did not differ significantly between the different types of AF during the initiation of treatment. However, in the maintenance phase (at least 12 weeks after the initial diagnosis), patients with persistent AF incurred the highest costs, followed by patients with paroxysmal and permanent AF [47]. McBride et al. investigated the impact of anti-arrhythmic treatment on total costs. Costs were lowest in patients not receiving anti-arrhythmic treatment and highest for patients receiving rhythm control treatment, who were more likely to have paroxysmal AF [38]. Kim et al. investigated patients receiving rhythm control treatment and found comorbidity and valvular disease to be independent predictors of higher costs and discontinuation of therapy. Age was associated with decreased costs [32]. Ghatnekar and Glader separated AF patients into different age groups and found 669

670

MCO

n.s.

n.s.

Third-party payer

n.s.

n.s.

n.s.

n.s.

NHS

NHS Scotland

n.s.

n.s.

n.s.

Medicare

National health insurance; society

Healthcare payer

Hospital sector

Medicare

SHI

SHI


Bhave et al. (2012)


Coyne et al. (2006)






Keech et al. (2012)

Kim et al. (2009a)

Kim et al. (2009b)

Kim et al. (2011)

Lee et al. (2008)



O‘Reilly et al. (2013)




[26]

[39]

[25]

[18]

[38]

[37]

[24]

[27]

[32]

†

[31]

[47]

[19]

[40]

[45]

[22]

[36]

[21]

[30]

[23]

Ref.

Others

Productivity loss

[42]

Long-term care

Rehabilitation

Pharmaceuticals

[34]

GP visit

Outpatient/ specialist

Hospital

Inpatient pharmaceutical only. GP: General practitioner; MCO: Managed care organization; NHS: National Health Service; n.s.: Not stated; SHI: Statutory health insurance.

†

USA Healthcare payer

Amin et al. (2011)

Perspective

Table 2. Cost components included in the cost-of-illness studies on atrial fibrillation.


Review Becker


[28]

[33]

[20]

[29]

[43]

[44]

[35]

Productivity loss Long-term care

Private health insurance; employer Wu et al. (2005)

Third-party payer Sussman et al. (2013)


† Inpatient pharmaceutical only. GP: General practitioner; MCO: Managed care organization; NHS: National Health Service; n.s.: Not stated; SHI: Statutory health insurance.

NHS Stewart et al. (2004)

Employer Rohrbacker et al. (2010)

n.s. Rostagno et al. (2010)

n.s. Ringborg et al. (2008)

USA Reynolds et al. (2007)

Rehabilitation Pharmaceuticals

excess costs to increase in age but to be lowest in the highest age group [36]. Likewise, Reinhold et al., who also performed a high utilizer analysis, found AF patients within the lowest quartile of total costs to be significantly older and have significantly fewer comorbidities than people within the highest quartile of total costs [39]. Reinhold et al. separately calculated costs by gender and found women to incur higher costs [26]. Boccuzzi et al. found higher costs in patients with outcome events or with greater stroke risk. Warfarin use was associated with higher costs but with lower costs in patients with outcome events [30].

gives an overview of the number of shared study characteristics for every possible pair of studies. There is one line for for every study and the studies serving as basis of comparison are listed in the columns. In total, 28 included studies were compared, this equals to 378 two-way comparisons (for more clarity, in this table, there are two records for every two-way comparison). Of these, 167 (44%) resulted in at least five different characteristics, indicating a high degree of heterogeneity among the studies. Studies from the same country, using the same type of data and the same methods of calculating costs may exhibit a higher degree of comparability; therefore, these studies are highlighted in TABLE 6. Overall, there were 20 such pairs of studies (5%). This table can be used to identify pairs of comparable studies, as starting point for more detailed analyses. TABLE 7 gives an overview of the quality of reporting. In general, most studies provided the information on most of the items, only four studies did not provide information on more than one-third of the items. Information that was missing in most of the studies was ranges for the estimates using confidence intervals or credible ranges (missing in 21 studies), a sensitivity analysis (missing in 20 studies), a specification of the study perspective (missing in 11 studies) and the specification of the year of activity data and the price year (missing in 5 studies).

TABLE 6


GP visit

Review

Comparability & quality of reporting

Hospital Perspective

Table 2. Cost components included in the cost-of-illness studies on atrial fibrillation (cont.).


Others

Ref.


Discussion

In this review, 28 cost-of-illness studies of AF were identified. Overall, it was found that additional healthcare expenditure was incurred due to AF. As to the relative importance of individual cost components, hospital care appeared to be the most important driver of the cost-of-illness of AF. Strategies aiming at preventing causes of hospital admission or reducing hospital costs could therefore serve as possible starting points for future economic evaluation of healthcare strategies [17]. Such strategies could relate to preventing cardiovascular comorbidity, such as stroke or heart failure, which was found to more severe in patients with AF [23]. There are different purposes to conduct cost-of-illness studies. While some stakeholders use high cost-of-illness estimates to direct attention toward a health condition, costof-illness studies can serve as starting point for further 671

[20]

[18]

[19]

115.25 538.79†

96.28

96.78

101.26

86.90

156.88

26.99

253.16 602.40 UK population (534,000 persons treated in 1995) Stewart et al. (2004)

687.05 687.05 Canadian population (64,214 admissions) O’Reilly et al. (2013)

267.23 267.23 Scottish population (54,686 AF-related discharges in 2008) Keech et al. (2012)

86.83 Swedish population (139,536 AF patients at an assumed prevalence of ~1.52%) Ericson et al. (2011)

991.59

38.80

2,291.85 276.20 1,798.22 3,443.65 7,815.78 USA population (343,131 visits) Coyne et al. (2006)

† Direct cost of complication. AF: Atrial fibrillation.

Other Other direct costs AF as secondary diagnosis Pharmaceutical Outpatient Hospital inpatient Total costs Reference figure (country) Study (year)


Table 3. National-level cost estimates (in million 2012 US-dollars). 672

[22]

[21]

Becker

Productivity loss

Ref.

Review

analyses, as they point out how resources are currently being used in a certain population [48]. Almost all of the identified studies were funded by stakeholders from the pharmaceutical industry. When new treatments are to be introduced, cost–effectiveness analysis is needed to assess how efficiently funds are being spent. By providing inputs for decision-analytic models, cost-of-illness studies can support cost–effectiveness analysis; however, the reviewed costof-illness studies’ suitability for this purpose appears limited. In particular, study populations were recruited from selected service providers or from people insured with selected providers, which can be expected to not always be fully representative for the particular country. In addition, only few studies calculated costs from a societal perspective, which is recommended for economic evaluation [16]. Likewise, while currently only few cost-of-illness studies took into account the type of AF, decision-analytic models would require more detailed analyses of subpopulations, such as people with different clinical manifestations of AF, who are subject to different treatment approaches [26,40,49]. A further application of cost-of-illness information could be budget impact analysis, which is often conducted alongside cost–effectiveness analysis [50]. Unlike cost–effectiveness analysis, budget impact analysis assesses the affordability of health technologies within specific healthcare settings [51]. These analyses contrast scenarios of different mixes of interventions. Thereby, the cost-of-illness calculated for a healthcare payer’s population from this healthcare payer’s perspective could serve as the reference scenario. However, budget impact analysis requires detailed information on the current technology mix in terms of resource use, in order for subsequent budget impact models to model financial consequences of changing the technology mix. Given the heterogeneity observed in cost-of-illness studies of AF, users of these studies should carefully examine the study designs, as these may influence the estimated costs. A major factor appears to be the distinction between studies of administrative data and surveys. Cost-of-illness studies that are based on surveys usually calculate costs based on utilization from shortly before the survey was conducted for the included people. In contrast, studies of administrative data calculate the included peoples’ costs for 1 year after their index date. If a study uses a long index period, such as in the study by Rohrbacker et al., where an indexing period of 10 years was used [29], the calculated annual costs per person originated from different calendar years and, although inflated to a common price year, do not necessarily reflect the current treatment mix. In addition, using administrative data, there is the danger of falsely identifying patients, which may result in bias toward any direction, unless there have been strategies toward validation, as found in several reviewed studies. Highly correlated with the analyzed type of data is the method of calculating the cost-of-illness: Calculating excess costs usually requires a control group and a complete recording of data, which is more easily obtained by using databases from insurance providers. In Expert Rev. Pharmacoecon. Outcomes Res. 14(5), (2014)


USA

Amin et al. (2011)

n.r.

Difference

n.r. n.r.

‡

‡

Control Difference

Difference

Control

AF

§

‡

1923

#

n.r.

n.r.

#

12,093

n.r.

14,016#

4220

3202

n.r.

7422‡

AF

12,787

19,822 19,191‡ 17,952‡ 17,256‡

Difference

3591

9040

16,415

5564

Control

9282

Difference

2796

28,863

12,758

Control

AF

22,039

AF

12,008 8360

n.r.

18,014 ††

n.r.

10,840† 6038‡

AF post-index

6006††

10,840 6038‡

14,830

25,734

7734

2905

10,639

Hospital

††

AF pre-index

Difference

†

14,830

Control

13,659

Difference 25,734

11,233

Control

POAF

24,891

Total costs

AF

Group

Includes direct costs only. Adjusted for possible confounders. Only for group of persons who had productivity loss. { Includes inpatient care and physician visits, not decomposed any further. # Includes only costs related to stroke. †† Original per member per month values multiplied by 12. AF: Atrial fibrillation; n.r.: Not reported; POAF: Post-operative AF.

†



Lee et al. (2008)

Kim et al. (2011)


Bhave et al. (2012)

Country

Study (year)

n.r.

n.r.

n.r.

n.r.

n.r.

n.r.

4088

3583

7563

3834

6002

9836

n.r.

n.r.

n.r.

5026

5430

10,456


Table 4. Individual-level cost estimates by country (incremental costs, in 2012 US-dollars).

n.r.

n.r.

n.r.

309

‡

518

‡

827‡

-116

3960

3844

n.r.

n.r.

n.r.

899

2898

3796

Outpatient pharmaceutical

n.r.

n.r.

n.r.

3663

‡,{

1951

‡,{

5614‡,{

2043

2090

4139

Other direct costs


248‡

733

‡

981‡

Productivity loss

[33]

[29]

[24]

[27]

[30]

[34]

[23]

Ref.


Review

673

674 Sweden 1359 560

Control (3 years) Difference (3 years)

1415

2996

Difference AF (3 years)

10,313

2384

4325† 788†,‡

Difference

AF post-index

7644

12,788† 18,206†,‡

Control

7315

10,028

17,117† 18,993†,‡

AF

AF pre-index

9554

19,143 18,992‡

Difference

560

1359

1415

2629

8048

5419

1373

4419

10,925

9887

Control

16,293 15,767‡

Difference

947

23,562

3565

Control

10,835

Hospital

AF (employees)

19,858

Total costs

AF (all individuals)

Group

Includes direct costs only. ‡ Adjusted for possible confounders. § Only for group of persons who had productivity loss. { Includes inpatient care and physician visits, not decomposed any further. # Includes only costs related to stroke. †† Original per member per month values multiplied by 12. AF: Atrial fibrillation; n.r.: Not reported; POAF: Post-operative AF.

†




Germany

USA

Wu et al. (2005)

Wu et al. (2005)

Country

Study (year)

101

353

252

87

686

759

5131

1431

6563

4215

1687

5901


205

1551

1345

30

1172

1202

1108

623

1731

1504

799

2,303

Outpatient pharmaceutical

Table 4. Individual-level cost estimates by country (incremental costs, in 2012 US-dollars) (cont.).

51

251

200

1820

4553

4923

623

83

707

686

132

817

Other direct costs


267

353

99

-2013§

6546§

4533§

2725

910

3635

Productivity loss

[36]

[26]

[42]

[28]

[28]

Ref.

Review Becker



Total AF

Hospital pharmacy only. AA: Anti-arrhythmic; AF: Atrial fibrillation; n.r.: Not reported; POAF: Post-operative AF.

†


Total AF


Poland

Total AF


Total AF


Italy

Total AF


1542

1082

4564

2054

2437

1034

n.r.

2688

n.r.

625

n.r.

Greece


1610

263

658

Maintenance phase (annual costs after 12 weeks) Total AF

964

1453

Initiation phase (first 12 weeks)

UK

3527

334

1344


801

No AA therapy/ cardioversion 4440

2314

Rhythm control treatment

531 203

Total AF

845

3158

2843

485

4298

199

5900

12,877

Hospital inpatient


1217

Rate control treatment

3494

6049

949

Total AF

Total AF



Total AF


Germany

Total AF

2479

Outpatient managed AF


7469

Secondary AF hospitalization

6843

16,096

Total direct costs

Primary AF hospitalization

Rhythm control treatment

USA

Kim et al. (2009)

Subgroup (if applicable)

Kim et al. (2009)

Country

Source

356

n.r.

1575

n.r.

1531

n.r.

273

377

n.r.

169

268

203

215

29

1089

434

1668

1630

1083

2480

Outpatient/ physician

Table 5. Individual-level cost estimates by country (disease-related costs, in 2012 US-dollars).

152

n.r.

301

n.r.

282

n.r.

76

14

793

234

529

305

336

183

2117

877

650

484

739

(Outpatient) pharmaceutical

46

99

119

32

199

134

137

125

Other direct costs


62

311

240

2150

Productivity loss

[44]

[45]

[44]

[45]

[44]

[45]

[47]

[39]

[38]

[40]

[35]

[25]

[32]

[31]

Ref.


Review

675

[40]

[43]

190

†

238

Hospital pharmacy only. AA: Anti-arrhythmic; AF: Atrial fibrillation; n.r.: Not reported; POAF: Post-operative AF.

POAF n.r.

†


2627

2437

274 2105 Total AF Sweden Jo¨nsson et al. (2010)

4038

118 1352 1131 2627 Total AF Ringborg et al. (2008)

Total AF The Netherlands Holstenson et al. (2011)

2061

n.r.

n.r.

n.r.

1430

3017

[44]

530

[45]

402 394 1412 1641 3448 Total AF Ringborg et al. (2008)

n.r. n.r. n.r. 2127 Spain Holstenson et al. (2011)

Total AF

Country

Subgroup (if applicable)

Total direct costs

Hospital inpatient

Outpatient/ physician

(Outpatient) pharmaceutical

Other direct costs 676

Source

Table 5. Individual-level cost estimates by country (disease-related costs, in 2012 US-dollars) (cont.).


[44]

[45]

Becker

Productivity loss

Ref.

Review

surveys or where no control group is available, it needs to be determined which utilization is attributed to AF. This was handled differently in the studies identified in this review. In studies that recorded utilization using retrospective patient questionnaires, there is potential memory bias. While such questionnaires can only include a limited amount of resources used, attributing administrative records to AF based on the associated diagnosis codes may provide a more complete picture, however, it is often unclear if the attribution is appropriate. One particular area of ambiguity is the attribution of anticoagulants, which are used to prevent stroke, which is, however, often caused by AF. A restrictive approach is likely to result in lower cost-of-illness estimates. The advantage of excess costs is that these avoid the ambiguity of attributing utilization to AF. However, inappropriate control groups can bias the excess costs in any direction and should therefore be adjusted to the covariate structure of people with AF. Overall, there was heterogeneity among the estimated costof-illness of AF (TABLE 6), highlighting the issue of comparability. There are several reasons why comparability is important: it is required for validation of study results, bringing together study results, and, furthermore, for facilitating the transfer of results to different settings. According to the pairwise comparisons conducted in this review, only few clusters of studies were from the same country, analyzed the same type of data and used the same cost-of-illness approach. Although there were several European studies, which estimated costs for different countries using the same methods across multiple countries, such as the study by Jo¨nsson et al. or the studies accompanying the European Heart Survey [40,44,45], comparability of studies across countries is limited [52,53]. In particular, cost-of-illness estimates from different countries are influenced by different treatment modalities and different pricing mechanisms [52]. Studies from the USA appeared to be better comparable among those analyzing Medicare data and those analyzing working populations; however, notable differences remained. Such differences first relate to the study perspective, which determines what costs are considered relevant. For example, Bhave et al. considered only one cost component, namely hospital costs [34]. In addition, the definition of which costs are related to AF appears to be important. For example, Pelletier et al., who considered utilization associated with principal diagnoses of AF only, found lower total costs than the other studies from the USA [25]. Furthermore, a cost-of-illness estimate is predominantly representative for the given study population. Differences in these populations influence the estimated cost-of-illness. While only few studies describe their populations with regard to the type and percentage of AFrelated utilization they received (TABLE 1), the general setting can give an indication: if the study setting was, for example, a hospital, patients with primary care contacts only would not be included, compared with other studies, this would possibly lead to higher estimated hospital costs. Furthermore, focusing on individuals with risk factors, such as history of stroke, is likely to result in higher cost estimates, whereas excluding Expert Rev. Pharmacoecon. Outcomes Res. 14(5), (2014)



people with risk factors, such as heart failure, is likely to result in lower cost estimates. Different population ages may, as for example analyzed by Rohrbacker et al., in addition, influence healthcare cost. Yet, it is unclear toward which direction this influence will go. Populations of higher ages, as in the studies of Medicare data, are more likely to be affected by cardiovascular comorbidity more than the general population. However, higher age was more often associated with permanent AF, which has been found to be associated with a higher functional status and a reduced severity of heart failure [49]. In addition, studies of younger populations are more likely to include productivity losses, as these individuals are more likely actively employed. Cost-of-illness, finally, reflect average treatment patterns prevalent at the time of the analysis. Although not visible in this review, changes in treatment guidelines, such as the increasingly promoted stroke prevention, may become apparent in a different ranking of cost components over time. Moreover, there were regulatory changes over time that affected even studies using the same perspective, as in the case of pharmaceutical reimbursement by Medicare [25,54]. As given constraints, such as research question or data availability, determine certain study characteristics, standardization across all study characteristics appears neither feasible nor desirable. Standardization should, therefore, be applied within study characteristics rather than across characteristics. Such differentiated standardization within studies could facilitate complementation and studies using the same set of characteristics could be investigated for validation purposes. There are several starting points for standardization within study characteristics. One could be a similar approach of identifying cases of AF using administrative data. Another is the method of calculating the cost-of-illness, currently a major source of ambiguity. While there have been arguments against the method of summing only disease-related costs, with limited data availability, this could remain the only feasible approach toward calculating the cost-of-illness [12,55]. However, ambiguity may arise when only selected resources are included in the cost calculation. With AF usually occurring in people with multiple comorbidities, ambiguity in attributing costs may introduce bias toward either direction [12]. As this was mostly done in survey-based studies, there, the use of a standardized definition of AF-related costs could especially increase comparability and strengthen credibility of the cost-of-illness estimates. A further area of standardization could be resource valuation, because, as described above, studies were not always consistent in pricing a given quantity structure according to a defined perspective. Standardized catalogs of unit costs could be a step forward in reducing the resulting ambiguity. Efforts in this direction have been undertaken previously in Germany and The Netherlands [55,56]. However, such catalogs would require continuous actualization, which poses a further obstacle towards a broader adoption. Overall, detailed reporting is paramount. By understanding the studies’ data sources and methodology, decision-makers can assess their applicability to a given decision problem. In addition, the comparability of the identified studies can be informahealthcare.com

Review

established. While in this review, according to the assessment of the reporting quality, the overall reporting quality was good. However, there are areas of improvement. A sensitivity analysis was missing in most studies. Varying the assumptions, for example, the disease definition in database studies or the valuation of unit costs, would allow the users of these studies to assess the robustness of the results [11,57]. Likewise, ranges for the results, for example, in terms of confidence intervals could strengthen the studies’ credibility. Other areas of improvement have direct implications on comparability: if the study perspective is not stated, it is unclear how the resources have been valued. For example, the perspective of health insurance providers does not include patient copayments. Furthermore, if the price year is not stated, it is not clear which year the results apply to. This is especially a problem in studies with a long time horizon that combine data from multiple years. Currently, two earlier reviews of cost studies for the indication of AF are used. These reviews were, however, conducted in 2009 and early in 2011 and the review by Wodchis et al. includes English publications only [58,59]. In addition, both studies focus on cost estimates, but do not detail the studies’ particular cost-of-illness methodology as it was aimed for in this review. Hence, this article provides a current overview of the cost-of-illness studies in AF and specifically focuses on the methods applied. There are limitations to this study. Even though the literature review was conducted using three well-accepted databases, it cannot be ruled out that expanding the search to further databases would have resulted in further relevant publications. While the literature review was carried out independently by two reviewers, the data extraction was carried out by one person and, therefore, reviewer bias cannot be fully eliminated. Furthermore, the adjustment of the extracted cost-of-illness estimates using purchasing power parities does not account for differences in quantities of services provided in different years. However, this adjustment provides a first step toward comparable prices and has been applied in the literature [53]. Limitations also apply to assessing the studies’ quality of reporting. While this assessment gives an impression on the quality of the publication, it does not allow drawing conclusions on the quality of the study itself or identifying the most reliable studies. However, as the publication is the medium of communication between researchers and stakeholders, the quality of reporting is paramount as it ensures that important features are transferred. With regard to the assessment of study comparability using pairwise comparisons, mainly constitutive characteristics were taken into account; therefore, a high number of corresponding characteristics is likely to provide an initial assessment of comparability of two studies. However, it should be understood as a qualitative valuation rather than a prescriptive rating, the relative importance of characteristics may differ. Conclusion

In this article, 28 cost-of-illness studies of AF were reviewed with a focus on methodological characteristics. Overall, the cost 677

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Becker


Table 6. Pairwise comparison of the characteristics of the cost-of-illness studies. [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

Amin et al. (2011)

[1]

11

5

7

4

5

3

2

3

4

5

3


[2]

5

11

5

3

4

0

4

3

1

5

1

Bhave et al. (2012)

[3]

7

5

11

3

3

2

4

2

2

5

2


[4]

4

3

3

11

4

3

2

6

7

2

2

Coyne et al. (2006)

[5]

5

4

3

4

11

4

1

4

3

2

4


[6]

3

0

2

3

4

11

2

4

7

3

5


[7]

2

4

4

2

1

2

11

2

3

4

1


[8]

3

3

2

6

4

4

2

11

7

3

3


[9]

4

1

2

7

3

7

3

7

11

5

5


[10]

5

5

5

2

2

3

4

3

5

11

5

Keech et al. (2012)

[11]

3

1

2

2

4

5

1

3

5

5

11

Kim et al. (2009a)

[12]

8

6

5

3

7

3

3

5

5

7

4

Kim et al. (2009b)

[13]

7

6

6

4

5

4

3

5

6

7

5

Kim et al. (2011)

[14]

8

5

6

4

4

2

3

5

5

7

4

Lee et al. (2008)

[15]

7

6

7

4

5

1

4

3

3

4

3


[16]

3

2

2

6

4

4

2

8

8

4

5


[17]

3

2

2

7

3

4

2

7

9

4

5

O’Reilly et al. (2013)

[18]

4

1

3

3

5

6

1

3

5

4

8


[19]

9

5

5

4

5

3

3

3

4

5

4


[20]

4

2

3

6

3

5

1

5

7

4

5


[21]

6

4

4

4

3

3

3

5

5

5

4


[22]

3

4

4

3

4

3

4

3

4

5

2


[23]

4

2

2

5

3

5

2

10

8

5

5


[24]

7

5

6

4

4

2

3

5

4

6

4


[25]

5

4

8

2

2

3

4

2

4

6

5

Stewart et al. (2004)

[26]

2

0

1

3

4

6

0

3

4

5

6


[27]

6

5

7

2

3

2

4

3

3

5

3

Wu et al. (2005)

[28]

7

6

6

5

6

2

3

5

3

4

3

Number indicates number of characteristics in agreement. Considered were ‘country of origin’, ‘study period’, ‘epidemiological approach’, ‘time horizon costs are estimated lation’. Highlighted studies have ‘country of origin’, ‘type of data analyzed’ and the ‘method of cost-of-illness calculation’ in common.

estimates varied, yet, hospital care was identified to be the single most important cost driver, providing a possible starting point for economic evaluation. While cost-of-illness estimates may be derived in order to raise attention toward a disease, they may also beneficiate the decision process as they can be used as an input to cost–effectiveness or budget impact analyses. Yet, it appeared that only few studies exhibited features needed for model-based economic evaluation. Combining studies, yet, requires comparability, which should be taken into consideration by scientists and decision-makers. The introduced 678

method of assessing the comparability of cost-of-illness studies showed to be feasible. The pairwise comparisons yielded a rather low overall comparability of the cost-of-illness studies, except for a small number of studies, which could serve as starting point for combining results or validation. In future, scientists conducting cost-of-illness studies should improve reporting with regard to conducting sensitivity analyses and reporting the adopted costing perspective. Efforts should be increased to provide more detail on subpopulations and the current treatment mix to increase their usefulness for cost–effectiveness or Expert Rev. Pharmacoecon. Outcomes Res. 14(5), (2014)



Review

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

8

7

8

7

3

3

4

9

4

6

3

4

7

5

2

6

7

6

6

5

6

2

2

1

5

2

4

4

2

5

4

0

5

6

5

6

6

7

2

2

3

5

3

4

4

2

6

8

1

7

6

3

4

4

4

6

7

3

4

6

4

3

5

4

2

3

2

5

7

5

4

5

4

3

5

5

3

3

4

3

4

2

4

3

6

3

4

2

1

4

4

6

3

5

3

3

5

2

3

6

2

2

3

3

3

4

2

2

1

3

1

3

4

2

3

4

0

4

3

5

5

5

3

8

7

3

3

5

5

3

10

5

2

3

3

5

5

6

5

3

8

9

5

4

7

5

4

8

4

4

4

3

3

7

7

7

4

4

4

4

5

4

5

5

5

6

6

5

5

4

4

5

4

3

5

5

8

4

5

4

2

5

4

5

6

3

3

11

10

8

6

5

5

4

6

3

6

3

5

8

5

2

7

7

10

11

9

6

6

6

5

8

5

6

6

6

8

5

3

7

8

8

9

11

8

4

4

4

7

3

7

5

5

8

5

2

6

7

6

6

8

11

3

3

2

7

2

5

4

3

7

5

1

7

7

5

6

4

3

11

8

4

4

6

6

3

9

6

3

6

3

4

5

6

4

3

8

11

4

4

8

7

3

8

5

3

4

3

5

4

5

4

2

4

4

11

4

5

4

2

5

4

5

5

3

3

6

8

7

7

4

4

4

11

0

0

2

0

1

5

1

5

2

3

5

3

2

6

8

5

0

11

5

4

7

3

4

4

3

3

6

6

7

5

6

7

4

0

5

11

1

5

7

5

2

5

6

3

6

5

4

3

3

2

2

4

1

11

4

2

3

3

3

3

5

6

5

3

9

8

5

0

7

5

4

11

5

4

4

3

5

8

8

8

7

6

5

4

1

3

7

2

5

11

5

2

7

11

5

5

5

5

3

3

5

5

4

5

3

4

5

11

2

6

4

2

3

2

1

6

4

5

1

4

2

3

4

2

2

11

2

2

7

7

6

7

3

3

3

5

3

5

3

3

7

6

2

11

6

7

8

7

7

4

5

3

2

3

6

3

5

11

4

2

6

11

for’, ‘type of data analyzed’, the studies’ ‘level of aggregation’, study ‘population characteristics’, ‘direction of recording cost data’ and the ‘method of cost-of-illness calcu-

budget-impact analyses. Overall, evaluating the cost-of-illness studies’ methodology according to the provided methods appears useful, as it allows researchers to complement studies and decision-makers to validate estimates. Expert commentary

There is an increasing awareness of the economic consequences of AF, which is the most common heart rhythm arrhythmia and associated with a highly elevated risk of stroke. Being increasingly funded by the pharmaceutical industry, cost-ofinformahealthcare.com

illness studies of AF raise attention toward this disease and may enrich corresponding health policy debate and reimbursement decisions. When cost-of-illness studies are to be used as starting points for further economic studies, such as cost–effectiveness or budget impact analyses, these studies’ characteristics need to be taken into account. While overall, the cost-of-illness studies of AF appeared heterogeneous, some studies appear more comparable than others. This is of interest, especially, for validation or if studies are to be combined. The assessment of comparability appears as an option to increase credibility of cost-of679

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Table 7. Quality of reporting.


Kim Kim Amin Bhave Boccuzzi Bru¨ggenju¨rgen Coyne Ericson Ghatnekar Holstenson Jo¨nsson Kassianos Keech Kim et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. (2007) et al. et al. and (2011) (2010) (2014) (2012) (2011) (2009a) (2009b) (2011) (2012) (2009) (2006) (2011) Glader (2008) Study question clearly specified?

Disease appropriately defined?

Study perspective stated?

ß

ß

ß

ß

ß

ß

ß

ß

ß

Epidemiological sources carefully described?

ß

ß

Sources of cost values analytically described?

ß

ß

ß

Method of cost calculation clearly described?

Sources of utilization data carefully described?

Year of activity data and price year stated?

Disaggregated results presented?

n.a.

ß

n.a.

ß

n.a.

Range of estimates presented?

ß

ß

ß

ß

ß

ß

ß

ß

Sensitivity analysis performed?

ß

ß

ß

ß

ß

ß

ß

ß

ß

Important limitations discussed?

Source of funding declared?

n.a.: Not applicable.

illness estimates and enhances their usefulness in more detailed economic analyses. Five-year view

As stroke prevention is increasingly being recognized as important goal in the treatment of AF, novel anticoagulants will gain in importance [60]. With the increasing role of economic inputs in healthcare decision-making, further cost-of-

680

illness studies will be conducted to highlight the economic impact of AF, especially in the context of reimbursement decisions. As beside cost–effectiveness, affordability is an important criterion in reimbursement decisions, budget impact analyses can be expected to gain importance [51]. Being readily available, cost-of-illness studies could become important as starting point for such analyses. In this context, it is imaginable that in future, cost-illness-studies will provide




Review

Lee Le McBride O’Reilly Pelletier Reinhold Reinhold Reynolds Ringborg Rohbacker Rostagno Stewart Sussman Wu Yes No et al. Heuzey et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. (2008) et al. (2009) (2013) (2005) (2012) (2011) (2007) (2008) (2010) (2010) (2004) (2013) (2005) (2004)

n.a.

ß

27

1

0

28

0

0

ß

ß

17

11

0

ß

ß

25

3

0

ß

ß

25

3

0

ß

ß

25

3

0

ß

ß

ß

23

5

0

ß

ß

26

2

0

n.a.

ß

21

4

3

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

7

21

0

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

ß

8

20

0

ß

ß

26

2

0

ß

ß

26

2

0

more detailed information on subpopulations and disease states as this would increase their usefulness for healthcare payers. Acknowledgements

The author would like to thank F Kirsch, who independently undertook the literature review for validation.


Financial & competing interests disclosure

The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.

681

Review

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Key issues • Atrial fibrillation (AF) is the most common hearth rhythm disorder and has considerable economic impact. • The term ‘cost-of-illness study’ labels a variety of methods that aim to calculate the additional costs due to to a particular health condition. • Scientists and decision-makers need to take into account the methodological choices in cost-of-illness studies in order to assess their


comparability and applicability to a given decsion context. • The 28 identified cost-of-illness studies on AF were heterogeneous, mostly calculating the sum of AF-related healthcare resources, rather than excess costs. • Mostly driven by the inpatient sector, the costs-of-illness of AF were high. Prevention efforts that aim at the prevention of inpatient costs appear as rewarding target for subsequent cost–effectiveness analyses. • For the reviewed studies, the overall quality of reporting was good, except for a clear definition of the study perspective and the conducting of sensitivity analyses. • The systematic approach to assess the cost-of-illness studies’ comparability proved feasible and pointed toward clusters of studies with a high comparability. However, standardization within study characteristics could enhance the cost-of-illness studies’ relevance. • Researchers and decision-makers could use the methods provided in this review to find comparable studies. This could aid model development, by pointing toward complementing studies and validation, by finding similar studies.

Rotterdam study. Eur Heart J 2006;27(8): 949-53

References Papers of special note have been highlighted as: • of interest •• of considerable interest 1.

2.

3.

4.

5.

Colilla S, Crow A, Petkun W, et al. Estimates of current and future incidence and prevalence of atrial fibrillation in the U. S. adult population. Am J Cardiol 2013; 112(8):1142-7 Krijthe BP, Kunst A, Benjamin EJ, et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J 2013;34(35):2746-51 Lip GY, Brechin CM, Lane DA. The global burden of atrial fibrillation and stroke: a systematic review of the epidemiology of atrial fibrillation in regions outside North America and Europe. Chest 2012;142(6): 1489-98 Chugh SS, Havmoeller R, Narayanan K, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014;129(8): 837-47 Wilke T, Groth A, Mueller S, et al. Incidence and prevalence of atrial fibrillation: an analysis based on 8.3 million patients. Europace 2013;15(4): 486-93

••

An exellent epidemiological study on atrial fibrillation in Europe.

6.

Heeringa J, Van der Kuip DAM, Hofman A, et al. Prevalence, incidence and lifetime risk of atrial fibrillation: the

682

7.

Leong DP, Eikelboom JW, Healey JS, Connolly SJ. Atrial fibrillation is associated with increased mortality: causation or association? Eur Heart J 2013;34(14): 1027-30

8.

Wolf P, Abbott R, Kannel W. Atrial fibrillation as an independent risk factor for stroke. The Framingham Study. Stroke 1991;22(8):983-8

9.

Wong CX, Brooks AG, Leong DP, et al. The increasing burden of atrial fibrillation compared with heart failure and myocardial infarction: a 15-year study of all hospitalizations in Australia. Arch Intern Med 2012;172(9):739-41

10.

Lloyd-Jones DM, Wang TJ, Leip EP, et al. Lifetime Risk for Development of Atrial Fibrillation: The Framingham Heart Study. Circulation 2004;110(9):1042-6

14.

Tarricone R. Cost-of-illness analysis. What room in health economics? Health Policy 2006;77(1):51-63

•

Features an informative overview of cost-of-illness methodology focusing on top-down and bottom-up methodology.

15.

Molinier L, Bauvin E, Combescure C, et al. Methodological considerations in cost of prostate cancer studies: a systematic review. Value Health 2008;11(5):878-85

Drummond M, Drummond M. Methods for the economic evaluation of health care programmes. Oxford University Press; Oxford, New York, NY, USA: 2005 ´ Ce´illeachair A, Hanly P, Skally M, et al. 17. O Cost comparisons and methodological heterogeneity in cost-of-illness studies: the example of colorectal cancer. Med Care 2013;51(4):339-50 16.

18.

O’Reilly DJ, Hopkins RB, Healey JS, et al. The burden of atrial fibrillation on the hospital sector in Canada. Can J Cardiol 2013;29(2):229-35

19.

Keech M, Punekar YS, Choy AM. Trends in atrial fibrillation hospitalisation in Scotland: an increasing cost burden. Br J Cardiol 2012;19(4):173-7

11.

Larg A, Moss JR. Cost-of-illness studies: a guide to critical evaluation. Pharmcoeconomics 2011;29(8):653-71

•

Well-written overview on the critical appraisal of cost-of-illness studies, featuring the draft of a checklist.

12.

Akobundu E, Ju J, Blatt L, Mullins CD. Cost-of-illness studies : a review of current methods. Pharmacoeconomics 2006;24(9): 869-90

20.

Stewart S, Murphy NF, Walker A, et al. Cost of an emerging epidemic: an economic analysis of atrial fibrillation in the UK. Heart 2004;90(3):286-92

13.

Clabaugh G, Ward MM. Cost-of-illness studies in the United States: a systematic review of methodologies used for direct cost. Value Health 2008;11(1):13-21

21.

Coyne KS, Paramore C, Grandy S, et al. Assessing the direct costs of treating nonvalvular atrial fibrillation in the United States. Value Health 2006;9(5):348-56




22.

Ericson L, Bergfeldt L, Bjo¨rholt I. Atrial fibrillation: the cost of illness in Sweden. Eur J Health Econ 2011;12(5):479-87

23.

Amin AN, Jhaveri M, Lin J. Incremental cost burden to US healthcare payers of atrial fibrillation/atrial flutter patients with additional risk factors. Adv Ther 2011; 28(10):907-26

24.

25.

26.

27.

28.

29.

Lee WC, Lamas GA, Balu S, et al. Direct treatment cost of atrial fibrillation in the elderly American population: a Medicare perspective. J Med Econ 2008;11(2):281-98 Pelletier E, Hernandez J, Clark M, et al. Medicare costs and health resource utilisation associated with atrial fibrillation in the elderly. J Med Econom 2005;8(1-4): 55-65 Reinhold T, Lindig C, Willich SN, Bruggenjurgen B. The costs of atrial fibrillation in patients with cardiovascular comorbidities – a longitudinal analysis of German health insurance data. Europace 2011;13(9):1275-80 Kim MH, Johnston SS, Chu BC, et al. Estimation of total incremental health care costs in patients with atrial fibrillation in the United States. Circ Cardiovasc Qual Outcomes 2011;4(3):313-20 Wu EQ, Birnbaum HG, Mareva M, et al. Economic burden and co-morbidities of atrial fibrillation in a privately insured population. Curr Med Res Opin 2005; 21(10):1693-9 Rohrbacker NJ, Kleinman NL, White SA, et al. The burden of atrial fibrillation and other cardiac arrhythmias in an employed population: associated costs, absences, and objective productivity loss. J Occup Environ Med 2010;52(4):383-91

••

The only cost-of-illness study on atrial fibrillation that includes reductions in on-site productivity.

30.

Boccuzzi SJ, Martin J, Stephenson J, et al. Retrospective study of total healthcare costs associated with chronic nonvalvular atrial fibrillation and the occurrence of a first transient ischemic attack, stroke or major bleed. Curr Med Res Opin 2009;25(12): 2853-64

31.

Kim MH, Lin J, Hussein M, et al. Cost of atrial fibrillation in United States managed care organizations. Adv Ther 2009;26(9): 847-57

32.

Kim MH, Klingman D, Lin J, Battleman DS. Patterns and predictors of discontinuation of rhythm-control drug therapy in patients with newly diagnosed atrial fibrillation. Pharmacotherapy 2009; 29(12):1417-26


33.

Sussman M, Menzin J, Lin I, et al. Impact of atrial fibrillation on stroke-related healthcare costs. J Am Heart Assoc 2013; 2(6):e000479

34.

Bhave PD, Goldman LE, Vittinghoff E, et al. Incidence, predictors, and outcomes associated with postoperative atrial fibrillation after major noncardiac surgery. Am Heart J 2012;164(6):918-24

35.

36.

Reynolds MR, Essebag V, Zimetbaum P, Cohen DJ. Healthcare resource utilization and costs associated with recurrent episodes of atrial fibrillation: the FRACTAL registry. J Cardiovasc Electrophysiol 2007;18(6): 628-33 Ghatnekar O, Glader EL. The effect of atrial fibrillation on stroke-related inpatient costs in Sweden: a 3-year analysis of registry incidence data from 2001. Value Health 2008;11(5):862-8

37.

Le Heuzey JY, Paziaud O, Piot O, et al. Cost of care distribution in atrial fibrillation patients: the COCAF study. Am Heart J 2004;147(1):121-6

38.

McBride D, Mattenklotz AM, Willich SN, Bruggenjurgen B. The costs of care in atrial fibrillation and the effect of treatment modalities in Germany. Value Health 2009; 12(2):293-301

39.

Reinhold T, Rosenfeld S, Muller-Riemenschneider F, et al. [Patients suffering from atrial fibrillation in Germany. Characteristics, resource consumption and costs]. Herz 2012;37(5):534-42

40.

Jo¨nsson L, Eliasson A, Kindblom J, et al. Cost of illness and drivers of cost in atrial fibrillation in Sweden and Germany. Appl Health Econ Health Policy 2010;8(5): 317-25

Review

45.

Holstenson E, Ringborg A, Lindgren P, et al. Predictors of costs related to cardiovascular disease among patients with atrial fibrillation in five European countries. Europace 2011;13(1):23-30

46.

Bruggenjurgen B, Reinhold T, McBride D, Willich SN. [Atrial fibrillation– epidemiologic, economic and individual burden of disease]. Dtsch Med Wochenschr 2010;135(Suppl 2):S21-5

47.

Kassianos G, Arden C, Hogan S, et al. The non-anticoagulation costs of atrial fibrillation management: findings from an observational study in NHS Primary Care. Drugs Context 2014;2014:212254

48.

Byford S, Torgerson DJ, Raftery J. Economic note: cost of illness studies. BMJ 2000;320(7245):1335

49.

Murin J, Naditch-Brule L, Brette S, et al. Clinical characteristics, management, and control of permanent vs. nonpermanent atrial fibrillation: insights from the RealiseAF survey. PLoS One 2014;9(1): e86443

50.

Mauskopf JA, Sullivan SD, Annemans L, et al. Principles of good practice for budget impact analysis: report of the ISPOR Task Force on good research practices – budget impact analysis. Value Health 2007;10(5): 336-47

•

Coherently summarizes standards for conducting budget-impact analyses.

51.

Cohen JP, Stolk E, Niezen M. Role of budget impact in drug reimbursement decisions. J Health Polit Policy Law 2008; 33(2):225-47

52.

Polder JJ, Meerding WJ, Bonneux L, van der Maas PJ. A cross-national perspective on cost of illness: a comparison of studies from The Netherlands, Australia, Canada, Germany, United Kingdom, and Sweden. Eur J Health Econ 2005;6(3): 223-32

53.

Welte R, Feenstra T, Jager H, Leidl R. A decision chart for assessing and improving the transferability of economic evaluation results between countries. Pharmacoeconomics 2004;22(13):857-76

41.

Scheuringer M, Sahakyan N, Krobot KJ, Ulrich V. Cost of clinical events in health economic evaluations in Germany: a systematic review. Cost Eff Resour Alloc 2012;10(1):7

42.

Bru¨ggenju¨rgen B, Rossnagel K, Roll S, et al. The impact of atrial fibrillation on the cost of stroke: the Berlin acute stroke study. Value Health 2007;10(2):137-43

43.

Rostagno C, La Meir M, Gelsomino S, et al. Atrial fibrillation after cardiac surgery: incidence, risk factors, and economic burden. J Cardiothorac Vasc Anesth 2010; 24(6):952-8

54.

Danzon PM, Wilensky GR, Means KE. Alternative strategies for Medicare payment of outpatient prescription drugs – Part B and beyond. Am J Manag Care 2005;11(3): 173-80

44.

Ringborg A, Nieuwlaat R, Lindgren P, et al. Costs of atrial fibrillation in five European countries: results from the Euro Heart Survey on atrial fibrillation. Europace 2008; 10(4):403-11

55.

Krauth C, Hessel F, Hansmeier T, et al. Empirische Bewertungssa¨tze in der gesundheitso¨konomischen Evaluation - ein Vorschlag der AG Methoden der gesundheitso¨konomischen Evaluation (AG

683

Review

Becker

MEG). Gesundheitswesen 2005;67(10): 736-46 56.


57.

Oostenbrink JB, Koopmanschap MA, Rutten FF. Standardisation of costs: the Dutch Manual for Costing in economic evaluations. Pharmacoeconomics 2002; 20(7):443-54 Costa N, Derumeaux H, Rapp T, et al. Methodological considerations in cost of

684

recent literature. Europace 2011;13(10): 1375-85

illness studies on Alzheimer disease. Health Econ Rev 2012;2(1):18 58.

Wodchis WP, Bhatia RS, Leblanc K, et al. A review of the cost of atrial fibrillation. Value Health 2012;15(2):240-8

59.

Wolowacz SE, Samuel M, Brennan VK, et al. The cost of illness of atrial fibrillation: a systematic review of the

60.

Larsen TB, Lip GY. Warfarin or novel oral anticoagulants for atrial fibrillation? Lancet 2014;383(9921):931-3


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