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Table 1 Public spending on orphan medicines reported in included literature

From: Public spending on orphan medicines: a review of the literature

Authors and year of publication Aim of the study Type of survey and expenditure data sources Included country/countries Year of data Spending data Sector OM spending Limitations reported in the studies
In absolute terms As percentage
Kanters TA, Steenhoek A, Hakkaart L (2014) [23] To assess uptake and budget impact of OM Secondary data analysis of GIP database (OP) and information by ph. c. or Monitor Expensive Drugs and FarmInform (IP) NL 2006–2012 Yes (OP) and no (IP), BI (predicttions) OP and IP 2006
OP: 52.7 mill. EUR
IP: 61.2 mill. EUR
OP: 68.7 mill. EUR
IP: 97.9 mill. EUR
OP: 97.8 mill. EUR
IP: 158.6 mill. EUR
OP: 118.1 mill. EUR
IP: 192.7 mill. EUR
OP: 141.6 mill. EUR
IP: 225.9 mill. EUR
OP: 156.2 mill. EUR
IP: 241.4 mill. EUR
OP: 175.2 mill. EUR
IP: 260.4 mill. EUR
2006: 1.1% of TPE
2007: 1.6% of TPE
2008: 2.6% of TPE
2009: 3.0% of TPE
2010: 3.6% of TPE
2011: 3.8% of TPE
2012: 4.2% of TPE
- Limited observation period (7 years)
Orofino J, Soto J, Casado MA, Oyagüez I (2010) [27] - To describe the status of orphan medicines in 2007 in the five countries in the EU with the greatest pharmaceutical expenditure
- To estimate the mean annual cost per patient and indication in relation to orphan medicines
- To determine the percentage contribution of orphan medicines to overall spending on medicines in each of these five countries in 2007
Secondary data analysis of IMS Health, MIDAS database DE, ES, FR, IT, UK 2007 No, sales data n.a. - 2007
FR: 1.7% of overall pharmaceutical expenditure
DE: 2.1% of overall pharmaceutical expenditure
IT: 1.5% of overall pharmaceutical expenditure
ES: 2.0% of overall pharmaceutical expenditure
UK: 1.0% of overall pharmaceutical expenditure
- Pharmaceutical costs only had been considered (no direct or indirect treatment costs)
- Calculations based on regimen in SPC
- Short assessment period of pharmaceutical expenditure (1 year)
- Prevalence data not completely trustworthy
- Standardised information provided by IMS Health, MIDAS database (data collection method used in each country could be biased)
Hutchings A, Schey C, Dutton R, Achana F, Antonov K (2014) [28] - To examine historical trends in OM designation, market authorization, sales and budget impact from 2000 to 2012
- To predict the evolution in OM use for existing diseases and new indications between 2013 and 2020
Secondary data analysis of GERS (France) and IMS Health, MIDAS database (France) FR, SE 2004
No, sales data n.a. - FR:
2012: 3.1% of TPS
2006: 0.7% of TPS
2012: 2.5% of total pharm. market value
- Forecasting assumptions
Schey C, Milanova T, Hutchings A (2011) [25] To estimate the European budget impact of orphan medicines as a percentage of total pharmaceutical expenditure, between 2010 and 2020, based upon 10 years of orphan medicine experience in Europe. Secondary data analysis, data source not indicated AT, BE, CY, DE, EE, ES, FI, FR, EL, IE, IT, LU, MT, NL, PT, SK, SI, UK 2010 n.a. n.a. - 2010: Cumulative for all countries 3.3% of total pharmaceutical spending - Orphan disease rather than the individual orphan medicine used for modelling
- Prevalence data might be weak due to data source
- Used ex-factory prices may not reflect effective price paid
- Predictability of prices after patent expiry
- Pharmaceutical market growth rate, success rate and uptake rate may be uncertain
Denis A, Mergaert L, Fostier C, Cleemput I, Simoens S (2010) [22] - To calculate the impact of OM for 2008
- To forecast its impact over the following 5 years
Secondary data analysis of data in ministerial decrees, via NIHDI, Ministry of Economic Affairs, IMS Health BE 2008 Expenses estimated based on treatment costs n.a. 2008: 66.2 mill. EUR 2008: 1.9% of TPE - One product excluded due to missing information
- Pharmaceutical expenditure only (no total treatment costs considered)
- Products financed by a special fund not considered
- Possible lower prices in future not considered
Iskrov G, Jessop E, Miteva-Katrandzhieva T, Stefanov R. (2015) [29] To estimate the impact of OM on NHIF total pharmaceutical budget between 2011 and 2014 Secondary data analysis of NHIF BG 2011
Yes n.a. 2011: 31.6 mill. BGN
2014: 74.5 mill. BGN
2011: 6.0% of TPE
2014: 7.8% of TPE
None reported
Iskrov GG, Jakovljevic MM, Stefanov SS (2018) [30] To estimate the budgetary impact of rare disease medicines’ therapies from NHIF perspective for 2014 and 2016
- To compare the main cost drivers for this period
Secondary data analysis of NHIF BG 2014
Yes OP and IPc - 2014: 9.39% of TPE
2016: 9.25% of TPE
- Included both orphan and non-orphan medicines (rare disease indications used for analysis)
- Analysis with official list prices, therefore BI might be overestimated
Logviss K, Krievins D, Purvina S (2016) [31] To assess the budget impact of OM in Latvia and compare it with other European countries Secondary data analysis of NHS LV 2010–2014 Yes n.a. 2010: 2.1 mill. EUR
2011: 2.6 mill. EUR
2012: 3.1 mill. EUR
2013: 2.1 mill. EUR
2014: 2.6 mill. EUR
2010: 1.95% of TPM
2011: 2.16% of TPM
2012: 2.62% of TPM
2013: 1.83% of TPM
2014: 2.16% of TPM
- Payers’ expenditure perspective only
- Product costs exceeding a yearly limit of NHS are not considered (costs might be higher)
- Different approach for estimating the number of patients
Divino V, DeKoven M, Kleinrock M, Wade RL, Kaura S (2016) [32] To estimate the economic impact of OM in the period 2007–2013
- To extrapolate orphan medicine spending up to 2018
Secondary data analysis of IMS Health, MIDAS database US 2007–2013 No, sales data n.a. 2007: 15.0 bill. USD
2008: 17.1 bill. USD
2009: 19.4 bill. USD
2010: 23.1 bill. USD
2011: 26.1 bill. USD
2012: 28.0 bill. USD
2013: 30.0 bill. USD
2007: 4.8% of TPS
2008: 5.5% of TPS
2009: 6.0% of TPS
2010: 6.8% of TPS
2011: 7.5% of TPS
2012: 8.5% of TPS
2013: 8.9% of TPS
- No stratification between therapies for chronic and acute illnesses (potential long-term impact on payers’ expenditure)
- IMS Health, MIDAS database do not cover 100% of the market
- No generic orphan medicines considered
- Potential off-label use of orphan medicines not considered
Divino V, DeKoven M, Kleinrock M, Wade RL, Kim T, Kaura S (2016) [33] - To estimate the financial impact of OM on the TPE from 2007 to 2013 in Canada
- To extrapolate orphan medicine spend up to 2018
Secondary data analysis of IMS Health, MIDAS database CA 2007–2013 No, sales data OP and IP 2007: 610.2 mill. CAD
f2008: 669.2 mill. CAD
2009: 743.7 mill. CAD
2010: 818.1 mill. CAD
2011: 880.5 mill. CAD
2012: 989.6 mill. CAD
2013: 1,100.0 mill. CAD
2007: 3.3% of TPS
2008: 3.4% of TPS
2009: 3.6% of TPS
2010: 4.0% of TPS
2011: 4.4% of TPS
2012: 5.0% of TPS
2013: 5.6% of TPS
- IMS Health, MIDAS database does not cover 100 % of the market
- Custom methodologies
- Possible changes through policy adoption not considered
- Potential differences in indication approvals (no approval in the USA, not accounted for in the study)
- No generic orphan medicines considered
- Potential off-label use not considered
Kockaya G, Wertheimer AI, Kilic P, Tanyeri P, Vural IM, Akbulat A, Artiran G, Kerman S (2014) [34] To shed light on the use of OM in Turkey to aid further classifications of rare diseases and assessments of orphan medicines in the country Secondary data analysis of IMS Turkey and TITCK TR 2008–2010 No, sales data n.a. 2008: 135.7 mill. EUR
2009: 182.4 mill. EUR
2010: 208.5 mill. EUR
2008: 2% of TPE
2010: 3% of TPE
None reported
Hsu JC, Wu H-C, Feng W-C, Chou C-H, Lai EC-C, Lu CY (2018) [2] To examine 2003–2014 longitudinal trends in the prevalence and expenditure of rare disease s in Taiwan Secondary data analysis of NHIRD TW 2003–2014 Yes n.a. 2003: 13.2 mill. USD
2004: 17.7 mill. USD
2005: 21.5 mill. USD
2006: 30.8 mill. USD
2007: 41.3 mill. USD
2008: 49.2 mill. USD
2009: 54.6 mill. USD
2010: 61.8 mill. USD
2011: 72.7 mill. USD
2012: 91.5 mill. USD
2013: 104.9 mill. USD
2014: 122.0 mill. USD
2003: 0.35% of TPE
2004: 0.41% of TPE
2005: 0.50% of TPE
2006: 0.73% of TPE
2007: 0.99% of TPE
2008: 1.14% of TPE
2009: 1.21% of TPE
2010: 1.37% of TPE
2011: 1.51% of TPE
2012: 1.92% of TPE
2013: 2.06% of TPE
2014: 2.31% of TPE
- Nationwide approach instead of individual patients (no out-of-pocket payments or clinical outcomes considered)
- Focus on rare diseases in general (no analysis with regard to certain rare diseases except for 2 rare diseases)
Deticek A, Locatelli I, Kos M (2018) [35] To estimate patient access to different medicines for rare diseases from the comprehensive Orphanet list in various European countries in the past decade Secondary data analysis of IMS Health data AT, BE, BG, CH, CZ, DE, EL, ES, FI, FR, HR, HU, IE, IT, NL, NO, PL, RO, SE, SK, SI, UK 2014d No, sales data OP and IPe AT: 4 mill. EUR/inh.
BE: 11 mill. EUR/inh.
BG: 4 mill. EUR/inh.
CH: 12 mill. EUR/inh.
CZ: 2 mill. EUR/inh.
DE: 15 mill. EUR/inh.
EL: 0.2 mill. EUR/inh.
ES: 8 mill. EUR/inh.
FI: 7 mill. EUR/inh.
FR: 12 mill. EUR/inh.
HR: 3 mill. EUR/inh.
HU: 2 mill. EUR/inh.
IE: 7 mill. EUR/inh.
IT: 12 mill. EUR/inh.
NL: 7 mill. EUR/inh.
NO: 6 mill. EUR/inh.
PL: 1 mill. EUR/inh.
RO: 2 mill. EUR/inh.
SE: 9 mill. EUR/inh.
SI: 8 mill. EUR/inh.
SK: 6 mill. EUR/inh.
UK: 11 mill. EUR/inh.
- - IMS Health data might not reflect the actual access to orphan medicines in the studied countries
- Expenditures might be overestimated (products with more than one indication that are not for rare diseases)
- Sales data only included if sales was continuous over a certain time
- Number of patients in need of treatment might differ from country to country due to prevalence of diseases and potential prescribing restrictions
  1. Countries: AT Austria, BE Belgium, BG Bulgaria, CH Switzerland, CY Cyprus, DE Germany, EE Estonia, EL Greece, ES Spain, FI Finland, FR France, HR Croatia, HU Hungary, IE Ireland, IT Italy, LU Luxembourg, LV Latvia, MT Malta, NL Netherlands, NO Norway, PL Poland, PT Portugal, RO Romania, SK Slovakia, SI Slovenia, TR Turkey, TW Taiwan, UK United Kingdom, USA United States of America
  2. Currencies: BGN Bulgarian Lev, CAD Canadian dollars, EUR Euro, USD US dollars
  3. Other abbreviations: BI budget impact, bill. billion, GERS Groupement pour l’Elaboration et la Réalisation de Statistiques, France, GIP Drug Information Project database by Health Care Insurance Board, Netherlands, inh. inhabitant, IP inpatient, mill. million, NHIF National Health Insurance Fund, NHIRD National Health Insurance Research Database, NHS National Health Service, NIHDI National Institute for Health and Disability Insurance, n.a. not available, OM orphan medicine, OP outpatient, ph. c. pharmaceutical company, SPC summary of product characteristics, TITCK Turkish Medicines and Medical Device Agency—Türkiye I˙laç ve Tıbbi Cihaz Kurumu, TPE total pharmaceutical expenditure, TPM total pharmaceutical market, TPS total pharmaceutical sales
  4. a Data were observed in the period 2000–2012. Figures are solely available for the years 2004, 2006 and 2012
  5. b Data were observed in the period 2011–2014. Figures are solely available for the years 2011 and 2014
  6. c Inpatient data refer solely to oncology treatments
  7. d Data were observed in the period 2005–2014. Figure is solely available for the year 2014
  8. e The treatment sector related to sales data varied between countries