Cost-of-illness studies in nine Central and Eastern European countries.

Analyses by countries are presented in Table  1 . The most frequently used data source was a retrospective, self-completed resource use questionnaire (48%), followed by retrospective claims data analysis (14%) and prospective diary (14%). Sample sizes ranged from n  = 2 (small cohorts) to n  = 127,512 (large population-based study). Of the 58 studies included in the review, 26 (45%) presented aggregated results for each main cost category (i.e., direct medical, direct non-medical, and indirect). The majority of studies applied the societal perspective (52%), followed by the public payer perspective (17%). If reported, bottom–up (38%) and top–down (21%) methods were used for estimating the costs in the studies. Productivity losses were estimated in 47 (81%) studies; of them, the human capital approach and friction cost method were used in 34 (72%) and 11 (23%) studies, respectively, and the method was not specified in 11 (23%) studies. Studies that reported costs of informal care ( n  = 29) applied the proxy-good method (17%) or the opportunity cost method (10%), but the name of the applied method was not stated in most of them (69%). Unit costs were not reported at all in 58% of the studies. Eighty-three COI estimates were reported for 48 different diseases. Apart from rare diseases, multiple sclerosis caused the highest economic burden in terms of average total annual cost per patient in three countries (Austria €50,599, the Czech Republic €14,777, and Poland €12,343) [ 23 – 25 ]. In Hungary, schizophrenia (€15,187), and in Bulgaria, gestational diabetes (€32,263) were the most costly diseases [ 22 , 26 ]. Multi-country studies were conducted in nine diagnoses (rotavirus gastroenteritis, pneumonia, bladder cancer, hypoglycaemia, Duchenne muscular dystrophy, epidermolysis bullosa, Prader–Willi syndrome, cystic fibrosis, and haemophilia). One multi-country study (bladder cancer) was conducted in nine countries and another (hypoglycaemia) in six countries. Two studies were conducted (rotavirus gastroenteritis and pneumonia) in four countries and four studies (Duchenne muscular dystrophy, epidermolysis bullosa, Prader–Willi, and haemophilia) in two countries. The bladder cancer study involving nine countries resulted in mean total costs of €7421; however, costs differed significantly among countries, as the total cost was between €2320 (Bulgaria) and €16,479 (Slovenia). The direct medical cost ranged between €1090 (Bulgaria) and €8050 (Slovenia), and indirect cost varied between €912 (Bulgaria) and €6398 (Slovenia). The hypoglycaemia study was conducted in six countries, and the total overall societal cost per patient with diabetes was €11 and ranged between €5 (Bulgaria) and €18 (Slovenia) [ 27 ]. Rotavirus gastroenteritis and pneumonia studies were conducted in four countries and the average total costs were €541 and €764, respectively. Costs varied between €494 (Czech Republic) and €747 (Poland) in rotavirus gastroenteritis, and between €472 and €1111 in pneumonia. Duchenne muscular dystrophy, epidermolysis bullosa, Prader–Willi syndrome, cystic fibrosis, and haemophilia were studied in two countries (Hungary and Bulgaria) applying the same methodology in a European Commission founded rare disease study (BURQOL-RD project). Prader–Willi syndrome was the least costly (Bulgaria: €3842 Hungary: €12,532) and mucopolysaccharidosis was the most costly rare disease (Bulgaria: €77,414; Hungary: €25,326) [ 28 , 29 ]. Unique studies in more than one country were conducted in eight diagnoses, namely multiple sclerosis, dementia, Parkinson’s disease, rheumatoid arthritis, osteoporosis, chronic obstructive pulmonary disease (COPD), systemic sclerosis, and diabetes. Multiple sclerosis and diabetes were studied most often (four studies each), while three unique studies in three different countries were conducted in Parkinson’s disease and two unique studies in three different countries were conducted in cystic fibrosis. Two unique studies on both dementia and COPD were conducted in two different countries. In multiple sclerosis, there was a 4.1 times difference in total costs between Austria (€50,599) and Poland (€12,343) [ 24 , 30 ]. In diabetes, the highest direct cost was observed in Hungary (€1309) and the lowest total cost was observed in Bulgaria (€472) [ 31 , 32 ]. In Parkinson’s disease, there was a 3.3 times difference in total costs between Austria (€22,984) and the Czech Republic (€6970) [ 33 , 34 ]. In dementia, we found a 3.5 times difference in total costs between the Czech Republic (€2013) and Hungary (€671) [ 35 , 36 ]. The costs of COPD were similar in Bulgaria (€1839) and Romania (€2103) [ 21 , 37 ]. Adjusting costs for GDP per capita level, differences between countries decreased (Table  2 ). For instance, a 7.1-fold difference in bladder cancer and a 4.1-fold difference in multiple sclerosis were reduced to 2.4- and 1.5-fold, respectively. Comparing diseases with available cost estimates from more than one country (Fig.  2 ), a positive relationship was identified between costs and GDP per capita. Table 2 Cost-of-illness in nine CEE countries (€ 2017) Disease Country Study Costing year Sample size Perspective Resource use data source EUR/patient/year converted to € 2017 Total cost as % of GDP/capita Total costs Direct medical Direct non-medical Indirect costs I. Certain infectious and parasitic diseases (ICD A00–B99)  Acute gastrointestinal infections POL Czech et al. [ 87 ] 2009 NR Societal Interview-based prospective cohort, follow-up period = 4 weeks 196 77 16 103 1.7%  Clostridium difficile infection HUN Kopcsóné Németh et al. [ 95 ] a 2011 151 Hospital Retrospective chart review 656–1397 NR NR NR 5.2–11.1%  HIV infection AUT Grabmeier-Pfistershammer et al. [ 58 ] 2006 24 NR Retrospective chart review 28,572 NR NR NR 5.7%  Rotavirus gastroenteritis CZE HUN POL SVK Tichopad et al. [ 75 ] 2013 109 NR 112 115 Payer Retrospective chart review 494 324 747 597 NR NR NR NR NR NR NR NR NR NR NR NR 2.7% 2.6% 6.2% 3.8% II. Neoplasms (C00–D48)  Bladder cancer AUT BUL HRV CZE HUN POL ROU SVK SVN Leal et al. [ 59 ] 2012 NR NR Publicly available sources and claims data were combined 12,988 2320 6035 7266 4545 6757 3812 8677 16,479 7965 1090 2520 4511 2748 3466 1750 6143 8050 NR 3292 912 2725 1935 1061 2333 1548 1749 6398 30.9% 32.7% 51.1% 40.1% 36.1% 55.8% 39.7% 55.6% 78.5%  Breast cancer HUN Inotai et al. [ 41 ] 2012 127,512 NR Retrospective claims data 1622 NR NR NR 12.9%  Cervical cancer POL Dubas-Jakóbczyk et al. [ 88 ] 2012 NR Societal Publicly available sources and social insurance data were combined NR NR NR 8457,898 f NA  Colorectal cancer HUN Inotai et al. [ 41 ] 2012 118,235 NR Retrospective claims data 2010 NR NR NR 16.0%  Lung cancer HUN Inotai et al. [ 41 ] 2012 126,731 NR Retrospective claims data 2663 NR NR NR 21.1%  Prostate cancer HUN Inotai et al. [ 41 ] Brodszky et al. [ 40 ] 2012 2005 56,382 17,642 Payer Payer Retrospective claims data Retrospective follow-up cohort of claims data, follow-up = 8 years 1656 12,072 NR NR NR NR NR NR 13.1% 95.8%  Skin melanoma HRV Bencina et al. [ 71 ] 2011 NR Payer Modelling Stage 0: 104–stage 4: 4610 NR NR NR 1.0-39.1% VI. Diseases of the nervous system (G00–G99)  Alzheimer’s disease CZE Maresova et al. [ 73 ] 2014 NR NR Publicly available sources and claims data were combined NR 13,208 NR 73.0%  Dementia HUN CZE Érsek et al. [ 35 ]. Holmerová et al. [ 36 ] 2008 2014 88 119 Societal NR Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 671 2013 b 222 387 63 1769 b 5.3% 11.1% 238 b  Epilepsy HUN Péntek et al. [ 83 ] 2009 100 Societal Cross-sectional self-completed questionnaire 2650 885 465 1300 21%  Multiple sclerosis AUT CZE HUN POL Kobelt et al. [ 24 ] Dusankova et al. [ 23 ] Péntek et al. [ 30 ] Szmurlo et al. [ 25 ] 2005 2007 2009 2012 1019 909 68 NR Societal Societal Societal Societal Cross-sectional self-completed questionnaire Prospective cohort, follow-up = 3 ms Cross-sectional self-completed questionnaire Extrapolation from other country 50,599 14,777 13,115 12,343 21,788 7581 8744 5805 10,109 550 1576 510 18,399 6646 2696 6028 120.5% 81.6% 104.1% 102.0%  Parkinson’s disease AUT CZE HUN Campenhausen et al. [ 33 ] Winter et al. [ 34 ] Tamás et al. [ 85 ] 2008 2004 2009 81 100 110 Societal Societal Societal Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 22,984 6970 7257 13,833 4238 9151 2733 2534 30.9% 38.5% 57.6% 2586 2136 IX. Diseases of the circulatory system (I00–I99)  Acute myocardial infarction HUN Gulácsi et al. [ 80 ]. 2003 996 Societal Claims data NR NR NR 947 7.5%  Chronic heart failure POL Czech et al. [ 92 ] 2010 400 Public payer Interview-based prospective cohort, follow-up period = 4 weeks 1991 NR NR NR 16.5%  Coronary artery disease POL Jaworski et al. [ 89 ] 2005 2593 NR Cross-sectional self-completed questionnaire 2851 1365 NR 1486 23.6%  Subarachnoide bleeding BUL Georgieva et al. [ 18 ] a 2014 61 Hospital Prospective cohort NR 3685 NR NR 51.9% X. Diseases of the respiratory system (J00–J99)  Bronchial Asthma BUL Ivanova et al. [ 20 ] a 2014 112 Hospital cost Retrospective chart review 200–393 c 200–393 c NR NR 2.8-5.5%  COPD BUL ROU Kyuchukov et al. [ 21 ] a Stâmbu et al. [ 37 ] NR 2006 84 85 Hospital and patient NR Prospective cohort Interview data 1839 2103 898 2103 NR NR NR NR 25.9% 21.9%  Lower respiratory tract infection BUL Glogovska et al. [ 19 ] a NR 1441 ambulatory + 353 hospitalized Health system NR NR 1218 NR NR 17.2%  Pneumonia CZE HUN POL SVK Tichopad et al. [ 76 ] 2010 258 NR 198 315 NR Claims data Ages 50–64/> 65 1194/786 1009/686 714/472 1685/1111 Ages 50–64/age > 65 708/786 686/686 472/472 1190/1111 Ages:50-64/> 65 486/0 323/0 242/0 495/0 6.6%/4.3% 8.0%/5.4% 5.9%/3.9% 10.8%/7.1%   Streptococcus pneumoniae ROU Stoicescu et al. [ 93 ] 2004 48,200 Public payer Claims data 8.3 million 8.3 million NR NR NA XIII. Diseases of the musculoskeletal system and connective tissue (M00.0–M99.9)  Chronic non-specific back pain AUT Wagner et al. [ 64 ] a 2008 48 Public payer Retrospective self-completed questionnaire 2148 1687 461 NR 5.1%  Osteoporosis SVN AUT Dzajkovska et al. [ 94 ] Dimai et al. [ 62 ] 2003 2008 NR 441/population-based Societal NR Publicly available sources and claims data were combined Publicly available sources and retrospective self-completed questionnaire were combined 34,524,727 d 827,849,562 d 24,432,069 d 520,419,423 d 1 10,092,657 d 307,430,139 d NA NA  Osteoarthritis of hip and knee AUT Wagner et al. [ 63 ] 2008 174 Public payer Retrospective self-completed questionnaire 3211 1342 1869 NR 7.6%  Rheumatoid arthritis CZE HUN Klimes et al. [ 72 ] Péntek et al. [ 86 ] 2014 2004 261 255 Societal NR Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 9176 5536 7442 1733 3034 50.7% 43.9% 1524 978  Systemic lupus erythematosus POL Kawalec et al. [ 90 ] 2012 1600 NR Claims data NR NR NR 1363 11.2%  Systemic sclerosis POL HUN HUN Kawalec et al. [ 90 ] Lopez Basida et al. [ 28 ] Minier et al. [ 82 ] 2012 2012 2006 500 38 80 NR Societal Societal Claims data Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire NR 4822 13,769 NR 1272 4724 NR 1184 1330 3394 2366 7716 28.0% 38.3% 109.3% IV. Endocrine, nutritional and metabolic diseases (E00–E90)  Diabetes BUL POL HUN SVN Valov et al. [ 32 ] Lesniowska et al. [ 91 ] Brodszky et al. [ 78 ] Nerat et al. [ 31 ] 2011 2009 2003 2011 433 NR 480 NR Payer Societal NR Payer Retrospective and prospective cohort, follow-up = 6 ms Claims data Cross-sectional self-completed questionnaire Publicly available sources 472 659 2514 NR NR 287 1309 882 NR 152 1118 NR 6.6% 5.4% 20.0% 4.2%  Hypoglycaemia HUN BUL HRV CZE POL SVN Jakubczyk et al. [ 27 ] 2013 2014 2012 2011 NR 2011 NR Public payer/societal Modelling 9.8 5.4 7.5 10.9 11.3 17.7 7.2 4.7 6.7 9.2 9.5 15.2 2.6 0.7 0.8 1.7 1.8 2.5 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% Other top level ICD items including < 2 disease  Benign prostatic hyperplasia HUN Rencz et al. [ 84 ] 2014 246 Societal Cross-sectional self-completed questionnaire 902 417 275 210 7.2%  Endometriosis AUT Prast et al. [ 60 ] 2009 73 Healthcare system Cross-sectional self-completed questionnaire 8945 6501 2443 21.3%  Gastro-oesophageal reflux disease AUT Willich et al. [ 61 ] 2000 5273 NR Prospective cohort, follow-up = 4 yrs 527 471 55 1.3%  Gestational diabetes BUL Todorova et al. [ 22 ] 2002-2005 195 Healthcare system Cross-sectional self-completed questionnaire 32,263 32,263 NR NR 454%  Psoriasis HUN Balogh et al. [ 77 ] 2013 200 Societal Cross-sectional self-completed questionnaire 9524 7816 152 1292 75.6%  Psoriatic arthritis HUN Brodszky et al. [ 78 ] 2007 183 Societal Cross-sectional self-completed questionnaire 7395 2489 1053 3853 58.7%  Sarcoidosis POL Kawalec et al. [ 90 ] 2012 2700 NR Claims data NR NR NR 1114 9.2%  Schizophrenia HUN Péntek et al. [ 26 ] 2009 78 Societal Cross-sectional self-completed questionnaire 15,187 4334 819 10,034 120.5% Rare diseases  Cystic fibrosis BUL CZE HUN BUL Iskrov et al. [ 70 ] Mlcoch et al. [ 74 ] Chevreul et al. [ 68 ] Chevreul et al. [ 68 ] 2012 2010 2012 2012 33 330 110 33 Societal NR Societal Societal Cross-sectional self-completed questionnaire Retrospective registry analysis Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 23,570 b 16,118 22,121 21,759 18,551 b 16,118 20,393 21,176 0 b NR 3802 1068 332.0% 89.0% 175.6% 306.5%  Duchenne muscular dystrophy HUN BUL Cavazza et al. [ 67 ] 2012 57 14 Societal Cross-sectional self-completed questionnaire 15,952 6500 15,094 2289 712 4211 145 0 126.6% 91.5%  Epidermolysis bullosa BUL HUN Angelis et al. [ 96 ] 2012 8 6 Societal Cross-sectional self-completed questionnaire 17,246 10,262 3503 438 13,485 9823 259 0 242.9% 81.4%  Fragile X syndrome HUN Chevreul et al. [ 79 ] 2012 12 Societal Cross-sectional self-completed questionnaire 5180 116 5065 0 51.6%  Haemophilia BUL HUN Cavazza et al. [ 66 ] 2012 20 58 Societal Cross-sectional self-completed questionnaire 6500 15,952 2289 15,094 2326 158 0 145 91.5% 126.6%  Histiocytosis BUL Iskrov et al. [ 69 ] 2012 7 Societal Cross-sectional self-completed questionnaire 6668 1657 2865 2145 93.9%  Mucopolysaccharidosis BUL HUN Péntek et al. [ 29 ] 2012 2 10 Societal Cross-sectional self-completed questionnaire 77,414 25,326 46,229 699 31,185 19,862 0 5091 1090.3% 201.0%  Prader–Willi syndrome BUL HUN Lopez Basida et al. [ 28 ] 2012 8 5 Societal Cross-sectional self-completed questionnaire 3842 12,532 2489 325 1354 12,207 0 0 54.1% 99.5% a Study identified through hand-search of local, non-indexed journals b Median c Bronchial asthma + exacerbations, bronchial asthma + pneumonia, and bronchial asthma + bronhiectasia d Aggregated costs for the total population of patients Fig. 2 Total costs (euro 2017) and GDP per capita (2017): comparison of single-country and multi-country studies. a Single-country studies: each line represents one disease, and each dot represents one study and one country. b Multi-country studies: each line represents one study and one disease, and each dot represents one country Cost-of-illness in nine CEE countries (€ 2017) CZE HUN POL SVK 109 NR 112 115 494 324 747 597 NR NR NR NR NR NR NR NR NR NR NR NR 2.7% 2.6% 6.2% 3.8% AUT BUL HRV CZE HUN POL ROU SVK SVN 12,988 2320 6035 7266 4545 6757 3812 8677 16,479 7965 1090 2520 4511 2748 3466 1750 6143 8050 3292 912 2725 1935 1061 2333 1548 1749 6398 30.9% 32.7% 51.1% 40.1% 36.1% 55.8% 39.7% 55.6% 78.5% Inotai et al. [ 41 ] Brodszky et al. [ 40 ] 2012 2005 56,382 17,642 Payer Payer Retrospective claims data Retrospective follow-up cohort of claims data, follow-up = 8 years 1656 12,072 NR NR NR NR NR NR 13.1% 95.8% HUN CZE Érsek et al. [ 35 ]. Holmerová et al. [ 36 ] 2008 2014 88 119 Societal NR Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 671 2013 b 63 1769 b 5.3% 11.1% AUT CZE HUN POL Kobelt et al. [ 24 ] Dusankova et al. [ 23 ] Péntek et al. [ 30 ] Szmurlo et al. [ 25 ] 2005 2007 2009 2012 1019 909 68 NR Societal Societal Societal Societal Cross-sectional self-completed questionnaire Prospective cohort, follow-up = 3 ms Cross-sectional self-completed questionnaire Extrapolation from other country 50,599 14,777 13,115 12,343 21,788 7581 8744 5805 10,109 550 1576 510 18,399 6646 2696 6028 120.5% 81.6% 104.1% 102.0% AUT CZE HUN Campenhausen et al. [ 33 ] Winter et al. [ 34 ] Tamás et al. [ 85 ] 2008 2004 2009 81 100 110 Societal Societal Societal Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 22,984 6970 7257 13,833 4238 9151 2733 2534 30.9% 38.5% 57.6% BUL ROU Kyuchukov et al. [ 21 ] a Stâmbu et al. [ 37 ] NR 2006 84 85 Hospital and patient NR Prospective cohort Interview data 1839 2103 898 2103 NR NR NR NR 25.9% 21.9% CZE HUN POL SVK 258 NR 198 315 Ages 50–64/> 65 1194/786 1009/686 714/472 1685/1111 Ages 50–64/age > 65 708/786 686/686 472/472 1190/1111 Ages:50-64/> 65 486/0 323/0 242/0 495/0 6.6%/4.3% 8.0%/5.4% 5.9%/3.9% 10.8%/7.1% SVN AUT Dzajkovska et al. [ 94 ] Dimai et al. [ 62 ] 2003 2008 NR 441/population-based Societal NR Publicly available sources and claims data were combined Publicly available sources and retrospective self-completed questionnaire were combined 34,524,727 d 827,849,562 d 24,432,069 d 520,419,423 d 1 10,092,657 d 307,430,139 d NA NA CZE HUN Klimes et al. [ 72 ] Péntek et al. [ 86 ] 2014 2004 261 255 Societal NR Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 9176 5536 1733 3034 50.7% 43.9% POL HUN HUN Kawalec et al. [ 90 ] Lopez Basida et al. [ 28 ] Minier et al. [ 82 ] 2012 2012 2006 500 38 80 NR Societal Societal Claims data Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire NR 4822 13,769 NR 1272 4724 NR 1184 1330 3394 2366 7716 28.0% 38.3% 109.3% BUL POL HUN SVN Valov et al. [ 32 ] Lesniowska et al. [ 91 ] Brodszky et al. [ 78 ] Nerat et al. [ 31 ] 2011 2009 2003 2011 433 NR 480 NR Payer Societal NR Payer Retrospective and prospective cohort, follow-up = 6 ms Claims data Cross-sectional self-completed questionnaire Publicly available sources 472 659 2514 NR NR 287 1309 882 NR 152 1118 NR 6.6% 5.4% 20.0% 4.2% HUN BUL HRV CZE POL SVN 2013 2014 2012 2011 NR 2011 9.8 5.4 7.5 10.9 11.3 17.7 7.2 4.7 6.7 9.2 9.5 15.2 2.6 0.7 0.8 1.7 1.8 2.5 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% BUL CZE HUN BUL Iskrov et al. [ 70 ] Mlcoch et al. [ 74 ] Chevreul et al. [ 68 ] Chevreul et al. [ 68 ] 2012 2010 2012 2012 33 330 110 33 Societal NR Societal Societal Cross-sectional self-completed questionnaire Retrospective registry analysis Cross-sectional self-completed questionnaire Cross-sectional self-completed questionnaire 23,570 b 16,118 22,121 21,759 18,551 b 16,118 20,393 21,176 0 b NR 3802 1068 332.0% 89.0% 175.6% 306.5% HUN BUL 57 14 15,952 6500 15,094 2289 712 4211 145 0 126.6% 91.5% BUL HUN 8 6 17,246 10,262 3503 438 13,485 9823 259 0 242.9% 81.4% BUL HUN 20 58 6500 15,952 2289 15,094 2326 158 0 145 91.5% 126.6% BUL HUN 2 10 77,414 25,326 46,229 699 31,185 19,862 0 5091 1090.3% 201.0% BUL HUN 8 5 3842 12,532 2489 325 1354 12,207 0 0 54.1% 99.5% a Study identified through hand-search of local, non-indexed journals b Median c Bronchial asthma + exacerbations, bronchial asthma + pneumonia, and bronchial asthma + bronhiectasia d Aggregated costs for the total population of patients Total costs (euro 2017) and GDP per capita (2017): comparison of single-country and multi-country studies. a Single-country studies: each line represents one disease, and each dot represents one study and one country. b Multi-country studies: each line represents one study and one disease, and each dot represents one country

As can be seen from Fig. S1 (online Appendix), after removing 246 duplicates, the search in the electronic databases resulted in 607 potentially relevant papers. Of these studies, 55 were not full-text papers and 98 were reviews. Furthermore, 282 papers did not report disease-related costs, 54 focused on costs of multiple diseases, and 67 focused on the cost of a certain treatment. Overall, 50 articles from the electronic search fulfilled the inclusion criteria. The supplementary local search resulted in another eight relevant articles in non-indexed, peer-reviewed journals (Austria: n  = 2, Bulgaria: n  = 5, and Hungary: n  = 1). Altogether, we included 58 articles (involving also multi-country studies) that reported results for Hungary ( n  = 24), Bulgaria ( n  = 16), Poland ( n  = 11), Czech Republic ( n  = 10), Austria ( n  = 9), Slovenia ( n  = 4), Croatia ( n  = 3), Slovakia ( n  = 3), and Romania ( n  = 3). Thirteen additional COI studies did not meet to our eligibility criteria (e.g., involved samples restricted by age, co-morbidity, complication, or treatment), but we found their results worthy of attention, and hence, a summary of their characteristics and main results is presented in online Appendix 1. The majority of publications reported costs from one country (74%), but 15 studies presented results from multiple countries, and hence, altogether, 83 country-specific results were provided by 58 studies (Table  1 ). Three-quarters of the studies were published in English ( n  = 44), and except for five papers [ 18 – 22 ], all non-English papers had an English abstract. Most of the publications (n = 45, 78%) presented costs in euro. In 37 studies, the national currency was converted to euro; of them, 17 (46%) studies stated explicitly exchange rate, 5 (14%) studies reported only the source of exchange rate, and 15 (40%) studies did not mention conversion at all. Among countries outside the euro zone, reporting costs in national currency was most common in Romania (67%). Overall, 47 (81%) studies stated the source of funding. The lack of a funding statement was most prevalent in Romania ( n  = 2, 67%) and in Bulgaria ( n  = 5; 31%). Only two studies received funds from two different sources, both of them were funded by the European Union (EU) and the local government. Regarding clinical areas, endocrine, nutritional, and metabolic diseases were the most common, in which costs were analysed ( n  = 15 country-specific results), followed by neoplasms ( n  = 12), and certain infectious and parasitic diseases ( n  = 10) (Fig.  1 ). Altogether 48 different diseases were analysed in the 58 included articles. Table 1 Characteristics of cost-of-illness studies Characteristic Number of country-specific results: N  = 83; Number of papers: N  = 58 1 Total a Austria [ 24 , 33 , 58 – 64 ] Bulgaria [ 18 – 22 , 27 – 29 , 32 , 59 , 65 – 70 ] Croatia [ 27 , 59 , 71 ] Czech Republic [ 23 , 27 , 34 , 36 , 59 , 72 – 76 ] Hungary [ 26 – 30 , 35 , 41 , 59 , 65 , 67 , 68 , 75 – 86 ] Poland [ 25 , 27 , 59 , 75 , 76 , 87 – 92 ] Romania [ 37 , 59 , 93 ] Slovakia [ 59 , 75 , 76 ] Slovenia [ 27 , 31 , 59 , 94 ] Total number of studies 9 16 3 10 24 11 3 3 4 58  English 5 11 3 10 21 11 1 3 4 44  National language 4 5 0 0 3 0 2 0 0 14 Search  Electronic database search 7 11 3 10 23 11 3 3 3 50  Hand-search 2 5 NA NA 1 NA NA NA NA 8 Currency  Euro 9 10 3 10 21 10 1 3 3 45  National currency NA 6 0 0 3 1 2 0 1 13 Source of resource use data  Retrospective cross-sectional, self-completed questionnaire 6 9 0 3 15 1 0 0 0 28  Retrospective chart review 1 1 0 2 2 1 0 1 0 5  Interview-based prospective cohort 1 2 0 1 0 3 1 0 0 8  Retrospective claims data 0 0 0 1 5 3 1 1 0 8  Combination of various sources b 1 2 1 2 1 2 1 1 3 6  Modelling 0 1 2 1 1 1 0 0 1 2  NR 0 1 0 0 0 0 0 0 0 1 Perspective  Public payer 2 2 2 2 2 3 0 1 2 10  Societal 2 8 0 3 18 4 0 0 1 30  Patient 2 0 0 0 0 0 0 0 0 2  Hospital 0 5 0 0 0 0 1 0 0 6  NR 5 1 1 5 4 4 2 2 1 13 Costing method  Top–down 1 1 0 1 1 2 0 1 0 12  Bottom–up 3 10 1 3 16 2 0 1 2 22  NR 5 5 2 6 7 7 3 1 2 24 Indirect cost calculation method  Human capital 5 8 0 3 18 7 0 1 1 34  Friction cost 1 1 1 2 1 1 1 1 1 11  NR 0 0 0 0 2 0 0 0 0 2  N/A 3 7 2 5 2 3 2 1 2 11 Informal care monetary valuation  Proxy good 0 8 1 0 16 1 0 0 0 5  Opportunity cost 1 1 0 3 2 1 1 1 1 3  NR 2 0 0 2 0 1 0 0 0 20  Other 1 0 0 0 0 0 0 0 0 1  N/A 5 7 2 5 6 7 2 2 3 29 Funding source  EU 1 8 0 0 9 1 0 0 0 13  Pharmaceutical industry 5 2 1 3 8 4 1 3 1 11  Government 1 0 0 5 3 1 0 0 0 13  Other 0 0 0 1 0 0 0 0 0 1  None 2 1 2 1 4 3 0 0 2 11  NR 1 5 0 0 0 3 2 0 1 11 Cost per patient reported  Direct medical costs 5 13 1 4 20 5 3 1 1 38  Indirect costs 6 10 2 6 21 9 1 2 3 38  Informal care cost 4 9 1 5 18 3 1 1 1 29  Total costs 8 13 3 7 23 9 3 3 3 47 Any unit costs  Reported 3 8 1 7 16 5 2 1 2 24  NR 6 8 2 3 8 6 1 2 2 34 NR not reported, N/A not applicable a Several studies published results for multiple countries. These studies are referred in each relevant country columns in a row, while, in the total column, a study might be referred only once in a row. Therefore, adding numbers in a row results in a larger sum than in the total column b Studies used combination of various sources of data: peer-reviewed published studies, national reports from governmental or professional bodies, extrapolations from similar countries, aggregated macrolevel data, claim data, and questionnaire survey Fig. 1 Distribution of COI studies by ICD classification. a Distribution of country-specific results across clinical areas defined by ICD groups ( n  = 83). b Distribution of studies between clinical areas defined by ICD groups ( n  = 58) Characteristics of cost-of-illness studies NR not reported, N/A not applicable a Several studies published results for multiple countries. These studies are referred in each relevant country columns in a row, while, in the total column, a study might be referred only once in a row. Therefore, adding numbers in a row results in a larger sum than in the total column b Studies used combination of various sources of data: peer-reviewed published studies, national reports from governmental or professional bodies, extrapolations from similar countries, aggregated macrolevel data, claim data, and questionnaire survey Distribution of COI studies by ICD classification. a Distribution of country-specific results across clinical areas defined by ICD groups ( n  = 83). b Distribution of studies between clinical areas defined by ICD groups ( n  = 58)

A systematic search was conducted to provide a review of the COI studies in nine CEE countries. The diffusion of the new technologies to the health scare systems is enormous, prices, and technologies, and professional guidelines are changing; therefore, our search was limited for the past 10 years. The included papers covered a broad range of clinical areas and showed notable cross-country differences in terms of methodology and publication standards as well as the average yearly costs per patient. Reporting cost results in euros was dominant over national currencies, suggesting that researchers in the CEE region find it important to make their results available for the international scientific community and allow for comparability with other studies. To assess study quality, we selected some quality indicators, such as those are used in health economics checklists. Reporting study perspective, reference year, costing method (top–down vs. bottom–up), source of resource use, valuation of informal care, valuation of productivity loss, and funding source were considered as quality indicators. We find it noteworthy to mention that whilst the source of data on resource utilization and reference year of costing were stated in nearly every paper (98% and 95%, respectively), other important quality indicators were less often reported. The study perspective was reported in 78%, the approach to valuing indirect costs in 77%, costing method in 64%, at least one unit cost in 42%, and method for valuing informal care in 31% of the studies. A recent review of economic evaluations in Austria found that the study perspective and reference year were not reported by 60% and 25% of the studies, respectively [ 13 ]. Differences may be explained by inclusion of non-peer-reviewed or grey literature (e.g., economic evaluation reports from national health technology assessment agencies) and of other forms of economic evaluations in the study by Mayer et al. The review by Mayer et al. included 93 (partial and full) economic evaluations, 14 of which were cost-of-illness analyses. Out of the 93 included studies, 23 were not indexed according to the Journal Citation Reports (Social) Sciences Edition and 12 were non-peer-reviewed reports [ 13 ]. A large variety of diseases was covered by the studies, and most of them occurred in a one study. Each disease was studied by, on average, 1.3 papers. Considering country-specific results by ICD categories, endocrine, nutritional, and metabolic diseases (18%), neoplasms (14%), infectious (12%), neurologic (11%), and musculoskeletal diseases (11%) represented the five main fields of COI research in CEE. It is difficult to judge the drivers of the selection of clinical fields. The public health importance of a disease might be an important factor as, for instance, all the studies in the ‘Endocrine, nutritional and metabolic diseases’ ICD category were related to diabetes, and among neoplasms studies, the most prevalent malignancies (breast, colorectal, lung, and prostate cancer) were present (Table  2 ). According to the Global Burden of Disease study, the leading three causes of total Disability-Adjusted Life Years (DALY) included ischaemic heart disease, cerebrovascular disease, and lower respiratory infection, comprising 16% of all DALYs [ 38 ]. Leading causes of DALYs were represented only in six (10%) studies (cerebrovascular disease: n  = 1, ischaemic heart disease: n  = 2, and lower respiratory infection: n  = 3) in our review, questioning public health importance as a driver of topic selection in COI studies. The need for COI data to support decision-making on reimbursement of highly effective but costly new drugs seems to be another relevant issue, and this hypothesis is supported by the relatively high rate of studies in inflammatory rheumatic diseases, where biological drugs were introduced in the CEE countries in the observed period. Multiple sclerosis is another disorder where biologicals revolutionized the treatment that partly explains the relatively high rate of neurological studies in the region. Moreover, when counting papers, neurologic diseases were most frequently studied (19%). A possible explanation could be that neurologic conditions in the CEE region were priorities for state-funded or EU-funded research. Eight out of the ten COI studies focusing on neurologic diseases received funding from the local governments or EU organisations. It is interesting that neurologic diseases were found also the most frequently studied clinical area according to a recently published systematic review of EQ-5D studies in the CEE region [ 39 ]. These results suggest that neurologic diseases have a high priority in health economics research in the CEE. With respect to diseases for which cost estimates were present in multiple countries, costs varied substantially across countries. However, there are apparent differences in the level of comparability between studies. There were multi-country studies following a standardized methodology in which more than one CEE country together with Western European countries was participated. We also identified single-country studies in various diseases using very different methods. Both multi-country and single-country studies reported significant cost differences in diseases across countries. For the interpretation of data, it is important to take into consideration that the number of patients, sample characteristics (e.g., age, gender, disease duration, and disease severity), and the availability of costly treatments at the time of the study (e.g., biological drugs for inflammatory diseases) varied a great deal across studies that may strongly influence the COI results and their comparability. Large differences in unit costs can also cause significant variations in costs. In bladder cancer, for example, the cost of an inpatient day was seven times higher in Austria (€495) than in Romania (€67). Methodological differences, such as prevalence- and incidence-based costing, form an obstacle for the comparison of costs. Therefore, the incidence-based prostate cancer study by Brodszky et al. cannot be compared with the prevalence-based prostate cancer study by Inotai et al., although both studies were conducted in Hungary [ 40 , 41 ]. It should also be noted that differences in health care systems (private/public, financing, etc.) might have a significant impact on costs; for instance, global budget, fee-for-service or DRG financing mechanisms, the presence of co-payments, minor or major share of private services, and many more aspects might influence the actual costs, access to health care, and, finally, the COI figure [ 42 ]. According to the literature, one might expect a higher COI in a country with a higher GDP [ 43 – 45 ]. In many diseases (multiple sclerosis, bladder cancer, Parkinson’s disease, rheumatoid arthritis, Prader–Willi syndrome, haemophilia, diabetes, and hypoglycaemia), there was a clear positive association between total costs and GDP per capita. As opposed to this, cost estimates, sometimes, inversely correlated with the per capita GDP. For instance, GDP per capita in Bulgaria is almost half of that in Hungary; nevertheless, costs of mucopolysaccharidosis were threefold higher in Bulgaria. Thus, in some cases, adjusting costs for the GDP further increased the inter-country differences. On the other hand, the 3.5-fold higher GDP per capita in Austria decreased the cross-country differences (from 4- to 1.3-fold) in costs of multiple sclerosis. In spite of the considerable heterogeneity observed in the studies included in this review, some trends could be identified. The magnitude of costs increased with the level of per capita GDP. In other words, cross-country differences decreased or even vanished when the costs were adjusted. In contrast, higher costs with lower GDP per capita could be observed only in some rare diseases (cystic fibrosis, epidermolysis bullosa, and mucopolysaccharidosis) and rotavirus gastroenteritis. Moreover, methodological differences did not seem to affect this relationship. Comparing multi-country studies in a disease applied the same methodology for more than one country and single-country studies analysed costs in the same disease, the relationship between cost-of-illness and GDP per capita showed similar pattern in these two groups of studies (see Fig.  2 ). Cost-of-illness studies varied considerably both in methods and in cost estimates, and serve many purposes. Methodological deficiencies, such as the lack of reporting either on the three distinct phases of costing (identifying the relevant cost items, measuring the use of the identified resources, and placing a value on these cost items) [ 46 ], or other important characteristics such as the perspective of the study, related to the production function (direct and indirect costs) were the leading causes of shortcomings in comparability. However, no specific costing guidelines for health care interventions are available in these countries, and except in Austria, there is no national cost database available, providing some kind of unit cost data in a collected form [ 13 , 47 , 48 ]. Another important difficulty in costing relates to the different Managed Entry Agreements (MEA), such as price volume agreements, discounts, outcome guarantees, and many more, in the reimbursement of the health technologies in the different countries [ 49 , 50 ]. Due to the MEAs, for instance, the real purchasing price of the medicinal products is not publicly available. Several papers were published about transferability in the past 2 decades [ 51 – 56 ]. At the moment, health economics and health technology assessment guidelines in CEE countries either include very limited advice or provide no guidance on the transferability or adaptation of clinical and economic data from other jurisdictions. Thus, establishing better guidelines for COI studies on transferability would be valuable for robust decision-making in the CEE countries [ 56 ]. As Gao et al. stated, confirming the transferability of COI estimates across jurisdictions would contribute significantly to resolving the issue of transferability of cost-effectiveness results [ 45 ]. Transferability is a very important issue around the world and especially in Central or Eastern Europe with limited resources to provide COI studies [ 53 – 55 ]. Data transferability and transferability of the results are not discussed in these COI studies. Both should be improved using Drummond’s check list for evaluating economic evaluations [ 57 ]. Transferability might be an important alternative to conduct local COIs. However, due to the methodological, data, and publication heterogeneity, the usefulness of the COI results in other jurisdictions is limited. There are a few limitations to note. A systematic approach was taken to identify studies that have considered the costs of diseases; however, the possibility that relevant studies were not identified and included in this systematic literature review remains. Some COI results might have been missed due to excluding grey literature (i.e., conference abstracts and project reports) from our search. Other limitationis that the local search in non-indexed journals was conducted only in three of the nine countries. On the other hand, no language restriction was applied in the systematic search. Adopting a Medical Subject Heading (MeSH)-based search strategy may have led to missing some studies using keywords improperly. At the same time, the PubMed search engine uses a broad range of entry terms which may minimize the number of excluded studies. Further limitation is that no comprehensive checklist was applied, because, according to our best knowledge, there is no COI study-specific checklist in English. This might bias our conclusions on study quality, but we believe that the presented study characteristics could give a good overall description of the included studies.

Fifty-eight COI studies were identified between 1 January 2006 and 30 June 2017 published in Austria, Bulgaria, the Czech Republic, Croatia, Hungary, Poland, Romania, Slovakia, and Slovenia, providing 83 country-specific COI results. Endocrine, nutritional, and metabolic diseases, neoplasms, infectious disease, and neurological disorders were the most frequently studied clinical areas. Transferability might be an important alternative to conduct local COIs. However, due to the methodological, data, and publication heterogeneity of these 58 COI studies, the transferability is limited across the nine Central and Eastern European Countries.

Cost-of-illness (COI) studies provide information on the economic burden of a specific disease from a societal, public payer, family or individual perspective. They aim to evaluate not only the disease-related healthcare costs but also the overall costs to society, including both medical and non-medical costs. COI studies can aid the understanding of the importance of a health problem, estimate the main cost components and the cost structure, and, thus, provide valuable cost estimates for use in full economic evaluations [ 1 ]. As a result, COI studies are an important type of health economic analysis aiming to support health policy and financing decision-making processes [ 2 ]. Over the past decade, health technology assessment has been implemented in most Central and Eastern European (CEE) countries, which, in turn, necessitates reliable, local country-specific COI studies [ 3 – 5 ]. There are no gold standard methods for calculating COI estimates [ 6 – 8 ]. Although standardization of the methods used in COI studies is becoming more and more important to allow comparability, studies apply different designs, methodologies, perspectives, and costing approaches [ 9 , 10 ]. Until now, several systematic reviews of COI studies have been conducted; however, most of them were focusing on one specific disease. Few reviews targeted a single specific cost item or component, such as informal care, direct medical costs, productivity loss, a specific geographic area, or a specific methodological aspect [ 10 – 13 ]. Nonetheless, COI studies from CEE countries have not been reviewed to date, with the exception of Austria [ 13 ]. This review has been undertaken to provide a description of the COI studies in nine CEE countries, namely Austria, Bulgaria, the Czech Republic, Croatia, Hungary, Poland, Romania, Slovakia, and Slovenia, in the past 10 years. The main objectives were to describe study characteristics, methodology, and the COI estimates reported. First, we provide an overview of applied methods. Then, we present and compare the COI estimates across CE countries.

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