The cost-effectiveness of centralizing
thrombolysis treatment in decentralized
stroke care systems
Roel Freriks
Master’s Thesis Economics
Department of Economics, Econometrics & Finance
University of Groningen
The cost-effectiveness of centralizing thrombolysis
treatment in decentralized stroke care systems
*Roel Freriks, B.Sc.
†Jochen Mierau, Ph.D.
‡Maarten Lahr, Ph.D.
§Abstract
Using Dutch data, the present study aimed to assess the cost-effectiveness of centralizing thrombolysis treatment in decentralized stroke care systems. The decentralized stroke care system consisted of nine community hospitals located in the Northern Netherlands. Discrete-event simulation was used to replicate current practice and estimate the effect of re-organizing decentralized stroke care. Centralization was considered cost-effective if the iCER – measured in Euros per DALY avoided – was less than three times the national annual GDP per capita. iCER confidence intervals were estimated using a non-parametric bootstrap method. The iCER for replacing the nine community hospitals by stroke centers was € 2,719 (95% CI, 2,671 – 2,793) per extra DALY avoided to € 1,741 (95% CI, 1,708 – 1,788) and € 1,387 (95% CI, 1,361 – 1,439) when centralizing to four and two hospitals. Sensitivity analysis of the input parameters travel time and mean annual costs – ± 25% relative to the base case – on the iCERs were performed. From the standpoint of cost-effectiveness, centralizing the nine community hospitals to two stroke centers seems to be the economically most attractive organizational model for acute stroke care in the Northern Netherlands to decrease the burden of stroke.
JEL classification: C15, I10, I31
Keywords: Acute stroke care, Simulation, Economic evaluation, DALY, Quality of life
* During my thesis I was fortunate to work with several experts in economic evaluating and acute
stroke care. Thanks to all members of this multidisciplinary team for making this research period an awesome experience. In particular, I would like to thank dr. Jochen Mierau for his valuable guidance and keen interest during the whole process. Secondly, I would like to thank dr. Maarten Lahr for making it possible to perform an economic evaluation of his research and his trust in me during the entire period. The cost-effectiveness analysis of this paper is an ad hoc analysis on the results of his doctoral thesis “Organizational models for thrombolysis in acute ischemic stroke: a simulation exemplar”. At last, I am also grateful to dr. ir. Durk-Jouke van der Zee and prof. dr. Erik Buskens for providing me with suggestions concerning the methodological aspects of my work.
† University of Groningen, Student, Email: r.d.freriks@student.rug.nl
‡ University of Groningen, Netspar Tilburg, First supervisor, Email: j.o.mierau@rug.nl
1
Figure 1.1: Global top 10 causes of death in 2012 (WHO, 2012a)
1.
Introduction
Stroke is the second cause of death worldwide, based on the latest World Health Organization (WHO) assessment of global deaths by cause in 2012, which is graphically depicted by Figure 1.1 (WHO, 2012a). Stroke is even the number one cause of death for upper-middle incomes1.
Although the mortality rates of stroke have decreased worldwide in the past two decades, Feigin et al. (2014) demonstrated that the overall burden of stroke – measured with disability-adjusted life years (DALYs) – is increased, based on the extensive report of the Global Burden of Disease Study 2010 (GBD 2010). Moreover, systematic analysis for the GBD 2010 showed that in 2010 even 4% of the 2.49 billion DALYs worldwide were due to stroke (Murray et al., 2012). If current trends in stroke incidence, mortality, and DALYs continue, by 2030 there will be almost 12 million stroke deaths, 70 million stroke survivors and more than 200 million DALYs lost globally (Feigin et al., 2014). In general, nowadays more people are saved from a stroke, but the overall burden of stroke is still increasing. Therefore, decreasing the burden of stroke is on the top of the political agenda of almost all developed countries. This paper aims to assess whether re-organizing the organizational model of acute stroke care decreases the burden of stroke in the Northern Netherlands in a cost-effective way.
1 The WHO demonstrated that the distribution of causes by death differs across income groups
2 The Central Bureau for Statistics (CBS) demonstrated earlier this year that stroke is also the second cause of death in the Netherlands (CBS, 2016). According to a Dutch report on the cost-of-illness, societal costs of stroke in the Netherlands totals around 1.63 billion Euros per year (Slobbe et al., 2011). Based on the exchange index of 0.77375 £ GBP per 1 Euro (European Central Bank, 2016), the average costs of care per stroke patient in the United Kingdom are currently 30,132 Euros in the first year (Stroke Association, 2016). This estimation is in line with the study of van Eeden et al. (2015) in which is estimated that societal costs incurred by a Dutch stroke patient are on average 29,484 Euros in the first year poststroke. If current trends in stroke continue, Struijs et al. (2006) expect the future costs of stroke in the Netherlands to grow even further the next years from 1.63 billion to approximately 2.08 billion Euros in 2020. Concerning the facts, stroke is the leading cause of death and disability in the world. Besides that, stroke substantially increases societal costs. Therefore, finding a way to decrease the burden of stroke – by increasing the DALYs avoided – and thereby keeping the costs at least within acceptable limits, would be a great contribution to acute stroke care.
The current treatment for stroke is the intravenous tissue plasminogen activator (tPA), also known as thrombolysis. Time is a critical factor for the efficacy of this treatment. Thrombolysis is the most effective within 4.5 hours after the onset of stroke symptoms and the efficacy is inversely proportional to time within these 4.5 hours (Boudreau et al., 2014; Petitta et al., 1998; Tung et al., 2011). However, previous studies have shown that thrombolysis is extremely underused (Adeoye et al., 2011; Wardlaw et al., 2009). Currently between 1-8% (Adeoye et al., 2011; Tung et al., 2011; Wardlaw et al., 2009) are treated with tPA worldwide and around 11% (ranging from 4-26%) within the Netherlands (Bauer et al., 2013), while 24-31% may be achieved in optimized settings (Lahr et al., 2012; Waite et al., 2006).
Lahr et al. (2013a) showed that improving organizational models for tPA treatment improves acute stroke care. More specific, Lahr et al. (2013b) used discrete-event simulation to replicate current practice and estimate the effect of re-organizing decentralized stroke care in the Northern Netherlands. Four set-up scenarios for the organizational model of acute stroke care in the Northern Netherlands were assessed, which are the comparators of the economic evaluation of this paper. The set-up scenarios are visualized in Figure 1.2. Scenario A is the current situation consisting of the decentral model with nine community hospitals. In Scenario B were the nine community hospitals of the decentral model replaced by nine stroke centers. In Scenario C were the nine community hospitals of the decentral model centralized to four, and in Scenario D to two stroke centers.
3
Figure 1.2: Acute stroke care set-up scenarios
whether centralization of thrombolysis is cost-effective for the included community hospitals of the decentral model. This paper aims to fill in this research gap. Centralization was considered cost-effective in this paper if the incremental cost-effectiveness analysis (iCER) – measured in Euros per DALY avoided – was less than three times the national annual gross domestic product (GDP) per capita2.
This paper proceeds as follows. Section 2 starts with a background on stroke. Subsequently, Section 3 describes the Dutch system of reimbursement decisions. Section 4 discusses the methodology, followed by a description of the data in Section 5. Section 6 contains the results and discussion. Finally, Section 7 offers some concluding remarks and policy recommendations. Further intermediate results and extra background information are represented in the Appendix.
2.
Background on stroke
The medical term for a stroke is cerebrovascular accident (CVA). A stroke occurs if a part of the brain is blocked for oxygen-rich blood. The lack of oxygen causes brain cells to die, which results in sudden loss of functions of parts of the body that are controlled by these brain cells. A stroke can be divided in two types based on the causation: hemorrhagic or ischemic. Figure 2.1 illustrates both types of stroke. A hemmorhagic stroke occurs when a blood vessel ruptures/hemorrhages, which abrupt the blood flow to that part of the brain. The most
2 There is a lack of an official threshold for cost-effectiveness in the Dutch quidelines for economic
4
Figure 2.1: Hemorrhagic and ischemic stroke
common cause for the rupture is uncontrolled high blood pressure. An ischemic stroke occurs when a blood vessel gets blocked by a blood clot, which prevents that part of the brain of getting oxygen-rich blood. This paper focuses on the latter type: ischemic stroke.
As mentioned before, thrombolysis is the current treatment of an ischemic stroke. Thrombolysis is in the Netherlands the only by Farmatec approved treatment for ischemic stroke3. Figure 2.2 shows how thrombolysis breaks down the blood clot that blocks the blood
vessel with the result that the blood flow to the deprived part of the brain improves. Again, thrombolysis is the most effective within 4.5 hours after the onset of stroke symptoms and the efficacy is inversely proportional to time within these 4.5 hours (Boudreau et al., 2014; Petitta et al., 1998; Tung et al., 2011). Hence, time is a critical factor for the chance of recovering from a stroke, which is clarified by the time-is-brain-concept of Saver (2006) in which the effect on the brain cells of a stroke is quantified4. Although the official medical term of stroke is
cerebrovascular accident5, many stroke specialists and researchers argue this designation. For
example, Ragoschke-Schumm et al. (2014) demonstrated that it is possible to have an influence on the time-delay of the patients until treatment by focusing on the pre-hospital stroke management. For this reason, several specialists and researchers believe that stroke should be called a cerebrovascular disease instead6.
3 For more detailed information on the process of approval and Farmatec, see Section 3.
4 Time-is-brain concept (Saver, 2006): In each minute poststroke, 1.9 million neurons, 14 billion
synapses, and 12 km (7.5 miles) of myelinated fibers are destroyed.
5 According to the dictionary, an accident is an undesirable or unfortunate happening that occurs
unintentionally and usually results in harm, injury, damage, or loss.
6 According to the dictionary, a disease is a disordered or incorrectly functioning organ, part,
5
Figure 2.2: Ischemic stroke treatment: tPA/thrombolysis
The impact of stroke on the quality of life (QoL) is concerning. Stroke takes a life every 25 minutes in the Netherlands, every 13 minutes in the United Kingdom and even every 4 minutes in the United States (Hartstichting, 2016; Stroke Association, 2016; Mozaffarian et al., 2016). A stroke causes disability, e.g. paralysis, speech and language disorders, concentration or memory problems, (partly) blindness, and behavioral or personality changes. These effects depend on the size and location of the affected area in the brains. Besides that, stroke has an substantial negative impact on the physical and mental wellbeing of the patient (Carod-Artal et al., 2000; van Eeden et al., 2015). Considering the impact of a stroke, the question pops up how it is possible that thrombolysis is extremely underused7. One of the reasons could be the
lack of familiarity of the patients with the symptoms of a stroke and the knowledge what to do8.
As discussed in Section 1, Lahr et al. (2013b) suggested to re-organize the organizational model of acute stroke care by centralizing thrombolysis. Indeed, centralization of thrombolysis led to a higher proportion of patients treated with thrombolysis in the Northern Netherlands. However, on which grounds can Dutch policy makers decide whether the health benefits (e.g. increased treatment percentage?) outweigh the corresponding costs?9
7 Recap: Currently between 1-8% (Adeoye et al., 2011; Tung et al., 2011; Wardlaw et al., 2009) are
treated with tPA worldwide and around 11% (ranging from 4-26%) within the Netherlands (Bauer et al., 2013), while 24-31% may be achieved in optimized settings (Lahr et al., 2012; Waite et al., 2006). 8 The National Stroke Association (2011) tries to work on this by paying attention to the Face Arm
Speech Time (FAST) campaign, which increases the awareness of the patients. Although the FAST campaign contributes to the time-delay of the patients until treatment (Robinson et al., 2013), it is not a solution to the problem.
6
3.
The Dutch system of reimbursement decisions
3.1. Reimbursement decisions of drugs
To provide the reader with an insight in the decision-making process of reimbursement decisions in the Netherlands, the expertise Pharmacoeconomics should be explained. Pharmacoeconomics can be defined as the branch of economics that uses economic evaluations to compare pharmaceutical products and treatment strategies (Arenas et al., 2005). The government regulates the reimbursement of drugs in the Netherlands. Farmatec, a part of the Dutch government, regulates the licenses, dispensations and registrations of medicines and medical devices for the pharmaceutical industry10. There are three different application
procedures before a new drug or treatment (e.g. thrombolysis) can be add to the Dutch drug reimbursement system, known as the Geneesmiddelvergoedingssysteem (GVS).
Route 1 (short procedure) is used when the proposed drug is a cheaper version of an existent drug, wherein Farmatec form the decision-making committee. Routes 2 (marginal test) and 3 (full procedure) are used when the drug is new on the market. In that case, Farmatec consult the therefore specialized part of the Dutch government, known as Zorginstituut Nederland (ZiN). ZiN takes care of the application of the new drug, making use of a plan which in general consists of four steps: 1. The pharmaceutical company submits evidence for the efficiency of the new drug, based on a regular medical investigation. 2. The Adviescommissie Pakket (APC) (Zorginstituut Nederland, 2015b), a committee within ZiN, provides a pharmaco-economical report in the form of an economic evaluation, adhering to the guidelines of economic evaluations in healthcare in the Netherlands (Zorginstituut Nederland, 2015c). This report demonstrates the achieved effects of the new drug plotted against the costs, from which the cost-effectiveness of the drug can be determined. 3. The APC and Farmatec report the findings of steps 1 and 2 to the Minister of department Ministry of “Volksgezondheid, Welzijn en Sport (VWS)” and provide the Minister with an decision-support advice whether the new drug should be reimbursed. 4. The Minister of VWS makes his decision, where after the drug can (cannot) be add to the GVS if the Minister approves (rejects).
3.2. Reimbursement decisions of re-organizing healthcare
Although the same quidelines as with determining cost-effectiveness of drugs are used for determining cost-effectiveness of a re-organization, there is no official policy on the rest of the procedure in the case of a proposed re-organization. For this reason, the board members of four of the nine included hospitals in this paper were requested to give their vision11. This
10 For more information, see www.farmatec.nl.
7 resulted in the following comparable steps before implementation of a new organizational model is possible: 1. The research team submits evidence for the efficiency of the re-organization, based on a regular medical investigation. The evidence should be supported and confirmed by multiple specialists in the corresponding discipline of the included hospital(s). 2. An independent committee of researchers in the field of expertise, possibly assigned by ZiN, provide a decision-support report in the form of an economic evaluation, adhering to the guidelines of economic evaluations in healthcare in the Netherlands (Zorginstituut Nederland, 2015c). 3. The board members of the included hospital(s) make simultaneously a decision and report the findings of steps 1 and 2 to the Minister of department Ministry of “Volksgezondheid, Welzijn en Sport (VWS)” and provide the Minister with an decision-support advice whether the new organizational model should be implemented. 4. The Minister of VWS makes his decision, where after the re-organization can (cannot) be launched if the Minister approves (rejects).
3.3. Economic evaluation in stroke
The process of reimbursement decisions in the Netherlands is already discussed in the previous subsections, where became clear that the APC (Zorginstituut Nederland, 2015b) of the ZiN uses the quidelines for economic evaluations in healthcare of the Dutch government (Zorginstituut Nederland, 2015c) to provide the needed (pharmaco-economical) reports12. To provide the
reader with insight in the procedure of economic evaluations, this last subsection discusses shortly the basic principles of an economic evaluation in healthcare.
Before an economic evaluation of a healthcare intervention is carried out, a framework should be defined and shaped by the researcher or practitioner. The economic evaluation should be performed and reported from a societal perspective. This means that all the relevant societal costs and benefits, regardless of who bears the costs or to whom the benefits accrue, are included in the evaluation and report. To complement the social perspective, the results can be presented from different perspectives, e.g. health perspective, financial perspective and organizational perspective. Drummond et al. (2015) argue that the relevance of the chosen perspective must be clearly motivated. Once there is clarity about the objective and the user's perspective, the specific research question of the economic evaluation should be formulated. The evaluation should be based on the requirements of PICOT, as described by the Centre for
12 The economic evaluation of this paper is based on the fundament of reimbursement analysis of
8 Evidence Based Medicine13. Subsequently, ZiN determines the relevance of the PICOT, making
use of a detailed report on the state of the science and practice (Zorginstituut Nederland, 2015a).
In general, a number of different analyses can be selected to perform an economic evaluation in which two or more different treatments are compared relative to each other. The most common used analyses for an economic evaluation are a cost-effectiveness analysis (CEA) or a cost-utility analysis (CUA). CEAs measure consequences in natural units, such as life years gained, disability days avoided or cases detected. CUAs are a variant of CEAs in which consequences are measured in terms of preference-based measures of health, such as quality-adjusted life years (QALYs) gained or disability-quality-adjusted life years (DALYs) avoided (Drummond et al., 2015). The advantage of CUAs comparing with CEAs is that it increases the comparability of the results between different diseases (Drummond et al., 2015)14.
The preference-based DALY metric developed by the WHO is the sum of years of life lost due to premature death (YLL) and years of healthy life lost due to disability (YLD): DALY = YLL + YLD (Murray et al., 2012). The cost-effectiveness decision of this paper is based on a threshold of Euros per DALY avoided15 since the DALY metric has several advantages over
conventional stroke outcome measures (Hong, 2011). As mentioned before, DALYs can easily be compared with other diseases and their treatments, which makes the DALY metric more intuitively accessible for public and health system planners. Thereby is the DALY metric statistically more powerful than either binary or ordinal rank outcome analyses in detecting the treatment effects of clinical trials. Finally, DALY explicitly indicates the burden of living with disability for an individual’s remaining life.
4.
Methodology
In this section, the analytical and empirical methodology is discussed, together with the structure and possible caveats. Several approaches to perform an economic evaluation use a cycle of common known steps before the cost-effectiveness can be determined (Briggs et al., 2006; Drummond et al., 2015). This cycle consists of nine steps, which are graphically depicted by Figure 4.1. These steps are the basis for the outline of the methodology of this paper. Steps
13 Patient = the patient or target population; Intervention = the intervention to assess; Control = the
intervention(s) to be compared; Outcome = the relevant results/outcome measures; Time = the relevant time period over which the effects and costs should be taken into account. For more details, see
www.cebm.net.
14 The Dutch government decided in the new guidelines for economic evaluations in healthcare of
November 2015 that an evaluation in preference-based health outcome measures (CUA) is required for an accepted economic evaluation (Zorginstituut Nederland, 2015c, p. 21).
9
Figure 4.1: The cycle of an economic evaluation (Drummond et al., 2015)
1 and 2 are already discussed in the previous sections. For completeness, a short recall. This paper aims to assess whether re-organizing the organizational model of acute stroke care decreases the burden of stroke in the Northern Netherlands in a cost-effective way. In this case, no alternative treatments are evaluated, but hypothetical scenarios for acute stroke care in the Northern Netherlands. The three hypothetical scenarios are (1) replacing the nine community hospitals by stroke centers, (2) centralizing the nine community hospitals to four stroke centers and (3) centralizing the nine community hospitals to two stroke centers.
The study design of the evaluation is based on a six-month prospective study of Lahr et al. (2012) focused on stroke patients in the Northern Netherlands, graphically depicted by Figure 4.2. As can be seen in Figure 4.2, the area of the three provinces in the Northern Netherlands are divided in a decentral and central model16. The original decentral model consists of nine
community hospitals in which patients can be treated with thrombolysis, known as Scenario A of the acute stroke care set-up scenarios. This model is the baseline model of the evaluation. At the hospitals of this model, general neurologists and neuroimaging were available 24 hours, 7 days per week. The decentral model with the nine community hospitals serves around 1,137,188 inhabitants with 181 inhabitants living per square kilometer. The original central model consists of four community hospitals in which patients cannot be treated with thrombolysis. The stroke patients of the central model are treated with thrombolysis by the
16 Note that I explain both the decentral and central model of Lahr et al. (2012) to demonstrate that
10
Figure 4.2: Current organizational model of acute stroke care in the Northern Netherlands
University Medical Center Groningen (UMCG), which operates as a central stroke center in this model. The UMCG provides 24-hour, 7-day access for the patients to consultations, neuroimaging at the emergency department (ED) and interventional neuroradiology. Besides that, stroke physicians are available for telephone consultations to the other hospitals in this area. The central model with the UMCG operating as central stroke center serves around 577,081 inhabitants with 247 inhabitants living per square kilometer. The difference in results between the decentral and central model (Lahr et al., 2012), which is displayed in Section 5, resulted in the idea to centralize thrombolysis in the decentral model.
11
Figure 4.3: The acute stroke pathway: key activities
As can be seen in Figure 4.3, three possible routes from stroke onset until treatment are distinguished in the model. In summary, Route 1 refers to a patient who calls 911 (Dutch: 112) or the general practitioner (GP) and is then transported to the hospital by the emergency medical services (EMS). A patient can be in Route 1 with a probability of 76%17. Route 2,
self-transport, assumes that patients are not transported to the hospital by the EMS, but taken by family/bystanders. A patient can be in Route 2 with a probability of 21%18. At last, Route 3
refers to patients who suffer a stroke while being in-hospital. A patient can be in Route 3 with a probability of 3%19.
Acute stroke pathway set-up was identical for the decentral and central model, despite the fact that the models are different from organizational perspective. Distances and access to healthcare services such as GPs offices and EMS are typically short in both models. Although the possibility of misdirecting patients decentral community hospitals based on wrong EMS assessments is not taken into account, previous research (Morris et al., 2014) and the prospective data of Lahr et al. (2012) on the studied region indicate how suchlike misinterpretations involve only 1-2% of the patient population
.
The EMS protocols of both17 (Lahr et al., 2013c): If the patient is in Route 1, then the following quantities need to be simulated
for modeling pre-hospital activities (Table A2): the time from symptom onset to call for help, the choice and time delay at the first responder (i.e. either the general practitioner or 911), the level of urgency set for EMS transport, the time between 911 activation and arrival of the ambulance at the location of the patient, the time spent by ambulance personnel at the location of the patient, and the time required to transport the patient to the hospital. Intra-hospital activities assume the following quantities to be simulated: the time from hospital arrival to neurological examination, the time required for neuroimaging (computed tomography, CT scan), the time to laboratory examination of patient blood samples, the time to reach a decision on patient treatment, and the time it takes to mix thrombolysis. 18 (Lahr et al., 2013c): The model is simplified with respect to the inclusion of patients in Route 2. As
all respective patients did not candidate for treatment in the real system, no quantities were simulated, except for their arrival at the emergency department. Note how Table A2 clarifies that all patients in this route, except for two, arrive way beyond the period of 4.5 hours after stroke onset, for which thrombolysis treatment has been found to be effective.
19 (Lahr et al., 2013c): If the patient is in Route 3 only intra-hospital time delays need to simulated,
12 models are similar: the EMS strives to arrive within 15 minutes from the 911 call until ambulance arrival at the location of the patient. Within all hospitals offering thrombolysis, the decision whether patients were eligible for tPA treatment was based on the same adjusted ECASS-3-protocol (Wahlgren et al., 2008). The adjusted ECASS-3-protocol appoint that eligible patients were only treated within the efficacy time window of the treatment, which is 4.5 hours after stroke onset20. Eventually, in the discrete-event simulation model 10,000
patients progressed along the stroke pathway, based on the model parameters and statistical distributions of table A1.
The cost of the alternative scenarios are identified, measured and valued as follows. The data for the costs were collected on resource use at the level of all individual patients, both in the pre-hospital and intra-hospital part of the acute stroke pathway. Mean annual and incremental costs per patient were estimated for all scenarios including fixed and variable costs. Fixed costs remained constant, while the patient volumes fluctuated. Logically, the variable costs fluctuated proportional to the change in patient volumes. Overall, the costs are short-term and are extrapolated to one year, which made it possible to estimate annual costs and patient throughput. Costs were assessed separately and presented as means with their corresponding 95% confidence intervals (CIs) for all scenarios.
The fixed costs consisted of (1) recurring annual public education campaigns, (2) staff education and (3) purchasing a new computed tomography (CT) scanner located in the emergency department (ED). All components of the included fixed costs are presented in Table 4.1. The variable costs consisted of (1) GP consultation both face-to-face and by telephone, (2) loan of medical personnel including a bonus of 39% and (3) deployment of ambulances: tariffs for emergency transport, EMS dispatch, and costs per driven kilometer. The deployment of medical personnel was assumed similar in case of centralization, based on expert judgement. All components of the included variable costs are presented in Table 4.2. Note that the patients are aligned along the pathway of Figure 4.3 based on the statistical distributions of Table A2, which naturally results in different mean annual costs per patient21.
20 Lahr et al. (2013c) demonstrated that the clinical benefit declines progressively over 4.5 hours after
stroke onset. For the simulation model the likelihood of treatment is approximated by a linear function, see Figure A2. A linear regression model (Y-axis intercept 97.5; slope -0.33) was used to approximate the chance of tPA treatment set against the overall process time for all patients arriving within 4.5 hours from the onset of stroke symptoms (i.e. eligible for tPA treatment).
21 For example, John called the GP after suffering a stroke and is taken to the hospital by the EMS
13
Table 4.1: Fixed unit costs for resource utilization
Resource Unit costs in Euros Source
Public education campaigns (range) € 3,364.44 (2,242.96 - 4,485.91)a Alberts et al. (2011)
Staff education (range) € 6,728.87 (4,485.91 - 8,971.83 )a Alberts et al. (2011)
Computed tomography scan
Purchase computed tomography scan € 1,000,000b Medisch Contact (2010) a The costs are presented in Euros, based on the euro-dollar exchange index of 1.1146 $US per 1 Euro (European Central
Bank, 2016).
b The new CT scanner was seen as an one-time investment and yearly depreciation costs for a new CT scanner were
conservatively estimated at 10% of the initial investment (Nederlandse Zorgautoriteit, 2012).
Table 4.2: Variable unit costs for resource utilization
Resource Unit costs in Euros Source
General practitioner
Telephonic consultation Visit by general practitioner
€ 27.79 € 42.67
Zorginstituut Nederland (2010)
Emergency medical services transport
Emergency transport Dispatch
Per driven kilometer
€ 668.18 € 53.79
€ 3.79
Data from regional ambulance services Groningen
Medical personnel emergency room visit
Medical specialist (15 minutes) Resident (1 hour) Nurse (1 hour) € 33.62 € 27.64 € 26.54 Zorginstituut Nederland (2010)
Outpatient clinic visit € 57.39 Zorginstituut Nederland (2010) Computed tomography scan € 170.46a Guzauskas et al. (2012)
Central laboratory (per test) € 24.31a Claes et al. (2006)
Alteplase € 753.76a Zorginstituut Nederland (2016)
a The costs are presented in Euros, based on the euro-dollar exchange index of 1.1146 $US per 1 Euro (European Central Bank, 2016).
14 case of centralization of thrombolysis treatment by hypothetically transporting patients from the emergency site to the nearest hospital offering thrombolysis, by applying the strategy using a web based route planner (Nallamothu et al., 2006; Nedeltchev et al., 2003) 22. Only the 446
patients of the decentral model transported by EMS were included in the calculation, because of availability of data on exact geographical locations for this group. The values obtained were adjusted to represent real-world data since the web based route planner does not account for faster driving speed achieved by ambulance transportation.
The secondary outcome measures extra healthy life days and DALYs avoided are translated from the primary outcomes measures, based on mapping standards in the literature. As mentioned before, the efficacy of thrombolysis is time-dependent. Therefore, the OTT can be translated into extra healthy life days: every minute reduction in OTT time results in an average 1.8 days of extra healthy life per patient (Meretoja et al., 2014). The DALYs of the scenarios are based on the tPA rate: every extra patient treated with thrombolysis within the time window of efficacy results in 4.4 extra DALYs avoided for that scenario (Hong & Saver, 2010). When data is collected or simulated on the effects and costs more than one year from the analysis, then the generated costs and effects should be discounted. In the Dutch quidelines of economic evaluations in healthcare, the discount rate of the costs are different from the discount rate of the benefits. Following the Dutch quidelines, the costs should be discounted at a constant discount rate of 4% and future benefits with a discount rate of 1.5% (Zorginstituut Nederland, 2015c, p. 21). However, these discount rates are questionable. Moreover, the Werkgroep Discontovoet (2015) proposed in their report that both discount rates should be adjusted to 3%. The Minister of department Finance has given his approval (Dijsselbloem, 2015), but this adjustment is not yet implemented in the quidelines of economic evaluations in healthcare (Zorginstituut Nederland, 2015c). In line with the latest research, the costs and benefits are adjusted for differential timing with a discount rate of 3% in this paper.
Putting the costs and benefits together and analyzing the results requires a cost-effectiveness threshold. Otherwise it is not possible to make a decision whether the proposed re-organization is cost-effective. However, there is no official cost-effectiveness threshold adopted in the Dutch quidelines of economic evaluations in healthcare. Based on the quidelines of the National Institute for Health and Care Excellence (NICE), the cost-effectiveness threshold should be $US 20,000 to $US 30,000 per extra QALY gained (Appleby et al., 2007). However, the NICE assumes that every QALY is equally valued, regardless of the burden of the disease. For this reason, the APC (Zorginstituut Nederland, 2015b) proposed to categorize diseases at their burden into three levels with corresponding cost-effectiveness thresholds. Following APC, a cost-effectiveness threshold of € 80,000 per extra QALY gained should be adopted for stroke. However, I chose to follow the age-standardized cost-effectiveness
15 threshold of the WHO: an intervention that, per DALY avoided, costs less than three times the national annual GDP per capita is considered cost-effective, whereas one that costs less than once the national annual GDP per capita is considered highly cost-effective (Marseille et al., 2015). The corresponding thresholds for the Netherlands are € 117,900 per DALY avoided (cost-effective) and € 39,300 (highly cost-effective) per DALY avoided (CBS, 2015).
The CIs of the ICERs were estimated using a non-parametric bootstrap method (Drummond et al., 2015), thereby building on simulation output data available for each of the scenarios. Travel times and distances were presented as medians with their corresponding 95% CIs. Mann-Whitney U and Fisher’s exact tests were performed for continuous and categorical variables. SPSS 20.0 for Windows software package (IBM, 2012) and Stata/SE 14.0 for Windows software package (StataCorp LP, 2014) were used. A p-value < 0.05 was considered statistically significant. To determine the validity of the decision, sensitivity analysis of the input parameter travel time was performed by determining how a change of ±25% in travel time and mean annual costs relative to the base case would influence the cost-effectiveness decision.
5.
Data and descriptive statistics
This section provides an overview of the data source and the sample selection method. The data stems from the study of Lahr et al. (2012). The data for this study is randomly collected between February and July 2010. The sample used consists of 1,432 patients, distributed over the decentral and central model. This is illustrated by the flow chart of Figure 5.1 where in the reasons for exclusion from tPA treatment are also provided. Of this cohort, 1,084 patients are diagnosed as ischemic stroke patients, whereof 351 patients were eligible for tPA treatment. Eventually, 175 patients received the treatment.
The descriptive statistics show that stroke severity did not differ across setting23. Concerning
all included ischemic stroke patients (Table 5.1), patients were more often a male and significantly younger in the central model. For all patients arriving within 4.5 hours (Table 5.2) and all patients treated with thrombolysis (Table 5.3), there were no differences in patient characteristics between both models. Most importantly, the tPA rate of the central model is 21.9% compared to 14.1% in de decentral model. This resulted into a corresponding odds ratio for likelihood of treatment with tPA of 2.03 for the central model versus the decentral model. This confirms the hypothesis of this paper that centralizing thrombolysis in decentralized stroke care systems should decrease the burden of stroke in the Northern Netherlands.
23 A full description and explanation of Tables 5.1 – 5.3 is beyond the scope of this paper. For more
16
Figure 5.1: Flow chart of the study
Table 5.1: Baseline characteristics of all ischemic stroke patients (Lahr et al., 2012)
Central model Decentral model
Number of patients 283 801
Age in years (SD) 70 (14) 73 (13)*
Male (%) 158 (56) 391 (49)*
Median sNIHSS on arrival (IQR) 1 (0-3) 1 (0-3)
Referral GP (%) 135 (48) 456 (57)
First responder EMS (%) 84 (30) 184 (23)* Transported by EMS (%) 213 (75) 460 (57)* High prioritization by EMS (%) 170 (80) 311 (68)* Median distance to hospital (km) 13.5 8.5**
* p < 0.05, ** p < 0.01; p < 0.001
17
Table 5.2: Baseline characteristics of patients arriving within 4.5 hours after stroke onset (Lahr et al., 2012)
Central model Decentral model
Number of patients (%) 124 (44) 227 (28)**
Age in years (SD) 69 (14) 71 (13)
Male (%) 63 (49) 124 (54)
Median sNIHSS on arrival (IQR) 2 (0-4) 2 (0-5)
Referral GP (%) 63 (51) 108 (48)
First responder EMS (%) 53 (43) 110 (48) Transported by EMS (%) 115 (93) 181 (80)* High prioritization by EMS (%) 109 (95) 162 (90) Median distance to hospital (km) 22.1 8.3**
* p < 0.05, ** p < 0.01; p < 0.001
SD indicates standard deviation; NIHSS, National Institutes of Health Stroke Scale; IQR, interquartile range; GP, General Practitioner; EMS, Emergency Medical Services; km, kilometer.
Table 5.3: Baseline characteristics of patients treated with tPA (Lahr et al., 2012)
Central model Decentral model
Number of patients (%) 62 (22) 113 (14)
Age in years (SD) 69 (16) 70 (14)
Male (%) 35 (56) 60 (53)
Median sNIHSS on arrival (IQR) 4 (2-7) 4 (2-7)
Referral GP (%) 27 (44) 35 (31)
First responder EMS (%) 31 (50) 71 (63) Transported by EMS (%) 59 (95) 98 (87) High prioritization by EMS (%) 55 (93) 94 (96) Median distance to hospital (km) 23.1 8.8**
* p < 0.05, ** p < 0.01; *** p < 0.001
18
6.
Results
In this section, the results of the economic evaluation are provided. Next, the corresponding effects of the results on the decision to re-organize acute stroke care in the Northern Netherlands are discussed respectively by assessing the corresponding iCERs. To determine the validity of the analysis, sensivity analyses of travel time and mean annual costs on the cost-effectiveness decision close this section.
First the time-related results of the simulation experiment per scenario are given in Table 6.1. The percentage of patients treated with thrombolysis (tPA rate) increases in all three hypothetical acute stroke care set-up scenarios. The tPA rate for the current decentral model of nine community hospitals is 14.4% (95% CI, 13.7 – 15.1). Compared to the current situation, replacing the nine community hospitals by stroke centers separately led to a tPA rate of 22.4% (95% CI, 21.6 – 23.2). Centralizing thrombolysis led to a tPA rate of 21.8% (95% CI, 21.0 – 22.7) when centralizing to four, and 21.2% (95% CI, 20.4 – 22.0) when centralizing to two stroke centers (p<0.01), respectively. The same pattern of improvement can be observed from the increase of percentage of patients treated within 0-90 minutes. The results confirm that centralization of thrombolysis results into a higher percentage of patients treated due to a decreased time-delay of the patients until treatment24.
24 Obviously, replacing all community hospitals of the decentral model by stroke centers (Scenario B)
results in the highest tPA rate. However, as mentioned before, Scenario B is add to the analysis in order to demonstrate that replacing a community hospital by a stroke center is of course always more effective.
Table 6.1: Time-related results of the simulation experiment
Scenario A:
decentral model with nine community hospitals
Scenario B:
decentral model with nine stroke centers
Scenario C:
central model with four stroke centers
Scenario D:
central model with two stroke centers
tPA rate (95% CI)
14.4% (13.7% - 15.1%) 22.4% (21.6% - 23.2%)** 21.8% (21.0% - 22.7%)** 21.2% (20.4% - 22.0%)** tPA 0-90 minutes 14.3% 27.5% 25.1% 21.6% tPA 90-180 minutes 70.5% 62.0% 63.2% 66.6% tPA 180-270 minutes 15.2% 10.5% 11.7% 11.9% * p < 0.05, ** p < 0.01; *** p < 0.001
19 Table 6.3 shows the mean annual costs and incremental costs per patient per scenario25. Mean
annual costs per patient for the current decentral model of nine community hospitals are €967 (95% CI, 955 – 980). Compared to the current situation, replacing the nine community hospitals by stroke centers separately led to mean annual costs per patient of €1,924 (95% CI, 1,913 – 1,935). Centralizing thrombolysis led to a mean annual costs of € 1,534 (95% CI, 1,523 – 1,546) when centralizing to four, and € 1,382 (95% CI, 1,370 – 1,393) when centralizing to two stroke centers (p<0.01), respectively. Total costs for the iCER are calculated by multiplying the mean annual cost per patient by 10,000. In line with the expectations, the three optional re-organizations come with incremental costs with the largest increase if all nine community hospitals are replaced by stroke centers (Scenario B).
Table 6.3 provides the health-related results of the simulation experiment. The first and second row of Table 6.3 show how time can be related to the health outcome measure extra healthy life days: every minute reduction in OTT results in an average 1.8 days of extra healthy life (Meretoja et al., 2014). This conversion factor was based on similarities between the population of the study of Lahr et al. (2012) and the one described in the literature (Meretoja et al., 2014) in terms of demographics and outcome distributions. Compared to the current situation, replacing the nine community hospitals by stroke centers resulted into an average 27.0 days of extra of healthy life per patient (95% PI, 13.5 – 40.5), compared to 21.6 days (95% PI, 10.8 – 32.4) and 16.2 days (95% PI, 8.1 – 24.3) when centralizing to four and two stroke centers (p<0.05), respectively. The iCER for replacing the nine community hospitals by stroke
25 Note that incremental costs are the difference in mean annual costs between one of the hypothetical
acute stroke care set-up scenario and the current situation.
Table 6.2: Cost-related results of the simulation experiment
Scenario A:
decentral model with nine community hospitals
Scenario B:
decentral model with nine stroke centers
Scenario C:
central model with four stroke centers
Scenario D:
central model with two stroke centers
Mean annual costs (€)
(95% CI) 967 (955 – 980) 1,924 (1,913 – 1,935)** 1,534 (1,523 – 1,546)** 1,382 (1,370 – 1,393)** Incremental costs (€)
(95% CI) 957 (952 – 962) 567 (563 – 571) 415 (411 – 418)
20 centers was € 35 (95% CI, 18 – 43) per extra healthy life day to € 26 (95% CI, 13 – 39) and € 26 (95% CI, 11 – 37) when centralizing to four and two stroke centers. Although the iCERs imply that as a result of centralizing thrombolysis the health of the patients improves with marginal increases in costs, the health outcome measure extra healthy life days cannot be used to make a decision on cost-effectiveness since it is not a preference-based measure of health. More can value can be attached to the results from the third row of Table 6.3 in which the change in tPA rate is conversed into the health outcome measure DALYs avoided: every extra patient treated with thrombolysis within the time window of efficacy results in 4.4 extra DALYs avoided for that scenario (Hong & Saver, 2010). As with the conversion factor for OTT into extra healthy life days, this conversion factor was based on similarities between the population of the study Lahr, et al. (2012) and the one described in the literature (Hong & Saver, 2010) in terms of demographics and outcome distributions too. Compared to the current situation, replacing the nine community hospitals by stroke centers resulted into an average 3,520 extra DALYs avoided per 10,000 patients (95% PI, 3,458 – 3,614), compared to 3,256 (95% PI, 3,196 – 3,343) and 2,992 DALYs (95% PI, 2,863 – 3,028) when centralizing to four and two stroke centers (p<0.01), respectively. The iCER for replacing the nine community hospitals by stroke centers was € 2,719 (95% CI, 2,671 – 2,793) per extra DALY avoided to € 1,741 (95% CI, 1,708 – 1,788) and € 1,387 (95% CI, 1,361 – 1,439) when centralizing to four and two stroke centers.
Table 6.3: Health-related results of the simulation experiment
Scenario A:
decentral model with nine community
hospitals
Scenario B:
decentral model with nine stroke centers
Scenario C:
central model with four stroke centers
Scenario D:
central model with two stroke centers
OTT minutes
(95% CI) 134 (131 - 136) 119 (117 - 127)* 122 (120 - 124)* 125 (123 - 127)* Extra healthy life days
(95% PI) a 27.0 (13.5 - 40.5)* 21.6 (10.8 - 32.4)* 16.2 (8.1 - 24.3)*
Extra DALYs avoided
(95% PI) b 3,520 (3,458 – 3,614)** 3,256 (3,196 – 3,343)** 2,992 (2,863 – 3,028)**
* p < 0.05, ** p < 0.01; *** p < 0.001
21 The iCER analyses indicates that substantial improvement in health outcome and life expectancy of the patients by re-organizing acute stroke care comes with marginal increases in annual costs. Furthermore, centralizing thrombolysis in the decentral model seems even highly cost-effective. However, policy makers should be cautious with interpreting these results, because it is extremely difficult to map out all the costs coming with acute stroke care (Evers et al., 2001), especially long-term cost components (van Eeden et al., 2012). Long-term cost components are more or less ignored in the analysis since the determination of these components came with too much uncertainty. For example, postponed therapy such as long-term sequela of ischemic strokes is not been taken into account. Besides that, costs associated with thrombolysis such as antithrombotic and lipid lowering medications were not part of the assessment and could therefore not be controlled for. Also the effects of improved patient outcomes on the frequency and intensity of informal (family) care should be assessed. Future studies should try to include more long-term variables in the economic evaluation, which makes it possible to extend the time horizon and through this assess the (discounted) costs and effects closer to real-life. Another limitation of the results is that productivity gains of centralizing thrombolysis to stroke centers are not assessed. For example, less personnel, work hours and materials might be needed due to the fact that stroke center designation reduces duplication of efforts and redundant diagnostic testing. Furthermore, an increase in the use of other hospital departments and services (e.g. radiology and laboratory services) might even induce increased revenues (Demaerschalk & Yip, 2005). Future studies should pay more attention to the possible productivity gain of centralizing thrombolysis.
However, the investments required for the community hospitals to re-organize acute stroke care should be quickly regained, despite the fact that some long-term cost components are not included in the analysis. For example, stroke patients rely extremely on nursing home, while providing nursing home comes with approximately 50,000 Euros per patient annually (Tan et al., 2012). The number needed to treat (NNT) with thrombolysis is 1 in 7 patients on average26.
All three hypothetical acute stroke care set-up scenarios assessed in this thesis led to substantial increased tPA rate. Therefore, combining these results with the NNT demonstrates that the new scenarios substantially keep out nursing home and the corresponding costs (Tan et al., 2012).
Baseline travel times and distances in case of centralization are presented as medians with their 95% CIs in Table 6.4. Overall, centralization of thrombolysis treatment resulted in travel times of 16.0 (95% CI, 3.0 – 31.9) and 21.2 minutes (95% CI, 4.0 – 37.7) in case of four and two stroke centers, compared to 12.0 minutes (95% CI, 2.0 – 30.0) in the baseline model (p<0.01).
26 NNT: Epidemiological measure used to determine the average number of patients who need to be
22 Travel distance increased to 19.7 km (95% CI, 1.1 – 42.5) and 26.4 km (95% CI, 1.4 – 54.5), compared to 11.6 km, 95% CI, 0.9 – 31.0) in the baseline model (p<0.01).
In terms of a cost-effectiveness plane (CE-plane), all three hypothetical acute stroke care set-up scenarios are more costly and more effective (dominance position) compared to the current situation. Moreover, the three scenarios are even highly cost-effective, based on the cost-effectiveness threshold of € 39,300 per DALY avoided. However, this threshold is not adopted in the Dutch quidelines of economic evaluations in healthcare, which implies that it is currently not possible to really make a cost-effectiveness decision in the Netherlands. Of course, this statement is oversimplified, but the reasoning is founded. However, the findings clearly indicate that re-organizing acute stroke care by centralizing thrombolysis is highly cost-effective.
To determine the validity of this assessment, sensitivity analyses of the input parameters travel time and mean annual costs were performed by determining how a change of ±25% relative to the base case would influence the cost-effectiveness decision. In case of
Table 6.4: Travel times and distances for the baseline case and centralization
Scenario B:
decentral model with nine stroke centers
Scenario C:
central model with four stroke centers
Scenario D:
central model with two stroke centers
Estimated travel time
Number of patients 446 446 446 Median (95% CI) 12 (2 – 30) 16 (10 – 22)** 21 (15 – 26)** < 5 minutes (%) 86 (19) 47 (10) 32 (7) 5-25 minutes (%) 337 (76) 333 (75) 278 (62) > 25 minutes (%) 23 (5) 66 (15) 136 (31)
Estimated travel distance
23 centralization of thrombolysis to four stroke centers, a decrease of 25% in travel time increased the number of patients treated with thrombolysis with 40 (from 2,183 to 2,223)27. An increase
of 25% in travel time decreased the number of patients treated with thrombolysis with 40 (from 2,183 to 2,143). In case of centralization of thrombolysis to two stroke centers, a decrease of 25% in travel time increased the number of patients treated with thrombolysis with 50 (from 2,116 to 2,166). An increase of 25% in travel time decreased the number of patients treated with thrombolysis with 60 (from 2,116 to 2,056). In case of centralization of thrombolysis to four stroke centers, a decrease of 25% in mean annual costs decreased the incremental costs by €383 (from €567 to €184). An increase of 25% in mean annual costs increased the incremental costs by €384 (from €567 to €951). In case of centralization of thrombolysis to two stroke centers, a decrease of 25% in mean annual costs decreased the incremental costs by €345 (from €415 to €70). An increase of 25% in mean annual costs increased the incremental costs by €346 (from €415 to €761). Concerning the cost-effectiveness threshold, the effects are substantial, but have no effect on the cost-effectiveness decision.
7.
Conclusion
This study demonstrated that all three hypothetical acute stroke care set-up scenarios led to higher mean annual costs for the included community hospitals, but also to an increased prolonged healthspan of the patients compared to the current situation. Centralizing the nine community hospitals of the decentral model to stroke centers may lead to substantial annual cost-savings per patient compared to replacing all nine community hospitals by stroke centers. Mean annual costs were least increased when reducing the nine of community hospitals to two stroke centers, while contrariwise replacing all nine community hospitals by stroke centers led to the highest decrease in the burden of stroke. From the perspective of an economist, the iCER determines the most cost-effective scenario. All three proposed re-organizations were highly cost-effective, based on the cost-effectiveness threshold of € 39,000 per DALY avoided. Small, but negative effects on DALYs avoided may be expected compared to the other two options, but centralizing to two stroke centers is the most cost-effective re-organization. Therefore, centralizing to two stroke centers seems to be the economically most attractive organizational model for acute stroke care in the Northern Netherlands to decrease the burden of stroke. This last part of the conclusion consists of three policy recommendations. First of all, I think that it is irresponsible to implement a new organizational model without consulting the specialists in stroke of the community hospitals involved. Their opinions about the re-organization might influence for example their productivity which could influence the results
27 Note that an increase of 40 patients treated with thrombolysis corresponds to a 0.4% increase in
25
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