CONSUMERS’ PERCEPTIONS OF HEALTH AND FACTORS INFLUENCING FULFILMENT OF THE NEED FOR HEALTHCARE IN EU COUNTRIES

19 3, XXV, 2022 Economics 10.15240/ ul/001/2022-3-002 CONSUMERS’ PERCEPTIONS OF HEALTH AND FACTORS INFLUENCING FULFILMENT OF THE NEED FOR HEALTHCARE IN EU COUNTRIES I ena An ošo á1, Naďa Hazucho á2, Jana S á ko á3 1 Mendel Uni e si y in B no, Facul y o Business and Economics, Depa men o Ma ke ing and T ade, Czech Republic, ORCID: 0000-0002-4331-4187, [email p o ec ed]; 2 Mendel Uni e si y in B no, Facul y o Business and Economics, Depa men o Ma ke ing and T ade, Czech Republic, ORCID: 0000-0002-5693-9872, [email p o ec ed]; 3 Mendel Uni e si y in B no, Facul y o Business and Economics, Depa men o Ma ke ing and T ade, Czech Republic, ORCID: 0000-0002-0889-0218, [email p o ec ed]. Abs ac : The pape deals wi h subjec i e pe cep ions o heal h by indi iduals. The esea ch aimed a unde s anding socioeconomic and demog aphic ac o s in luencing he ul ilmen o heal hca e needs and a inding ou ca ego ies o ac o s ha lead o he highes chances o mee ing he need in consume segmen s o med acco ding o pe cep ions o hei heal h s a us. The analyses we e based on he EU-SILC da abase o p ima y da a on he income si ua ion and li ing condi ions o households. In 2017, he da abase included ex a ques ions on heal h. The me hod o clus e analysis was employed. As a esul , h ee clus e s o indi iduals ep esen ing EU coun ies o med depending on he pe cei ed s a e o heal h – he au ho s named he clus e s ‘op imis ic’, ‘neu al’, and ‘pessimis ic’. Fo each segmen , he bina y logis ic eg ession was applied o de e mine ca ego ies o ac o s leading o he highes p obabili y o mee ing he heal hca e need. The g ea es in luence o e he ul ilmen o he need o heal hca e has been con i med o he ac o “Sec o o economic ac i i y”, ollowed by he ype o economic ac i i y. Some di e ences we e e ealed be ween segmen s. Fo example in he hi d segmen , i.e., esponden s who a ed wo s hei heal h, a s ong in luence o educa ion has been iden i ied. The highes chances o mee ing he need o heal h ca e a e achie ed in he i s segmen by execu i es, bu in he second and he hi d segmen by indi iduals ac i e in educa ion. On he o he hand, c a smen and wo ke s ha e he lowes chances. In all segmen s, he in luence o household composi ion was con i med, wi h single households and single-pa en households epo ing lowe chances o mee ing hei heal hca e needs. Responden s who did no eel hei heal hca e need was me mos ly said i was due o inancial easons, long wai ing imes, o ea o medical ea men . Keywo ds: Heal h, need o heal hca e, consume beha iou , income, household. JEL Classi ica ion: I31, P46. APA S yle Ci a ion: An ošo á, I., Hazucho á, N., & S á ko á, J. (2022). Consume s’ Pe cep ions o Heal h and Fac o s In luencing Ful ilmen o he Need o Heal hca e in EU Coun ies. E&M Economics and Managemen , 25(3), 19–34. h ps://doi.o g/10.15240/ ul/001/2022-3-002 In oduc ion The heal h is de ined as a s a e o a pe son’s physical, men al, and social well-being. Responsibili y o heal h is de e mined no only by he heal hca e sys em and gene ic p edisposi ions o indi iduals bu also by one’s li es yle and app oach o achie ing and keeping a good s a e o heal h (Wo ld Heal h EM_3_2022.indd 19 15.9.2022 13:48:54 20 2022, XXV, 3 Economics O ganiza ion, 2006). Consume beha iou conce ning heal hca e di e s om o he a eas, abo e all because i is “a ques ion o li e and dea h”. The e o e, his ype o decision-making ends o ge signi ican ly a ec ed by emo ions (Cazacu, 2015). Ano he signi ican di e ence is ha consume s ge heal hca e p oduc s and se ices h ough a hi d pa y, mos o en a physician, who ecommends s eps o be aken and makes he decisions (Radulescu e al., 2012). Kenkel (1990) s a es ha physicians can c ea e o educe demand o hei se ices. Mee ing heal h ca e needs is no always a ma e o consume choice, bu o he ac o s also play a ole. The main goal o he pape is o e eal socioeconomic and demog aphic ac o s in luencing he ul ilmen o EU consume s’ heal hca e needs and o ind ou ca ego ies o ac o s ha lead o he highes p obabili y o mee ing he need in consume segmen s o med acco ding o pe cep ions o hei heal h s a us. How an indi idual’s heal h is pe cei ed and, mos impo an ly, whe he heal hca e needs a e me when hey occu ha e been he basic esea ch ques ions o he pape . To lea n abou subjec i e iews on heal h, he au ho s used he EU-SILC su ey. The su ey p o ides da a on subjec i e pe cep ions o heal h as such, as well as in o ma ion abou mee ing he need o heal hca e and possible easons o no mee ing his need. The esul s o he analyses may ep esen a s ong a gumen o implemen ing imp o emen s in he heal hca e sys ems. This means in pa icula imp o ing access o heal hca e se ices o he majo i y o consume s. 1. Theo e ical Backg ound People s i e o mee hei heal hca e needs unde he condi ions se by he heal hca e sys em and he inancial esou ces hey ha e a ailable. The a i ude o a household o hei heal h and he use o heal hca e se ices a ec s he household’s li ing s anda d (Callande e al., 2019). Khan and Ul Husnain (2019) demons a ed ha heal hca e expendi u e and income a e co-in eg a ed and, he e o e, he e is a link be ween he s anda d o li ing, income si ua ion, and heal h s anda d. Lenha (2019) examined he e ec o income on he s a e o heal h and ound ha highe income inc eased he chances o excellen o e y good heal h being epo ed by households’ heads. The inc ease obse ed he e anged om 6.9 o 8.9 pe cen age poin s. Acco ding o Knaul e al. (2012), low-income households li ing nea he isk-o -po e y h eshold spen mo e on heal hca e. I means hei heal hca e expenses accoun ed o a highe pa o hei disposable income. Howe e , in absolu e numbe s, hey could a o d ewe heal hca e se ices han households in highe -income ca ego ies. Acco ding o Blumbe g e al. (2014), heal h- ela ed expendi u es a e ising as e han incomes, bo h a he na ional and household le els. Sha es o households’ disposable incomes spen on heal hca e a e inc easing. In coun ies whe e pa s o he popula ion ha e no heal h insu ance, he inancial demands o heal hca e could lead o pe sonal bank up cies. The subjec i e heal h in Cen al and Eas e n Eu opean coun ies is in luenced by a complex mix o de e minan s (Bo iso a, 2019). Di e ences in indi iduals’ socioeconomic s a uses (s emming om di e en economic ac i i ies, educa ion, o income ca ego ies) can con ibu e o heal h inequali ies and o chances o mee he heal hca e needs. Indi iduals wi h highe economic s a uses a e mo e in luenced by beha iou al and psychological ac o s in hei app oach o heal h han hose wi h lowe socioeconomic s a uses (A kinson & Ma lie , 2010; Pe e i-Wa el e al., 2016). Socioeconomic s a us a ec s heal h- ela ed quali y o li e (Pucia o e al., 2020). Sel -pe cei ed heal h is in luenced by income and labou s a us and by demog aphic ac o s such as gende o age in EU coun ies (Jind o á & Labudo á, 2020). Chaupain-Guillo and Guillo (2015) s a e ha demog aphic ac o s a e o he ac o s ha in luence consume s’ access o heal hca e. Gende is one o he ac o s a ec ing app oach o heal h- ela ed ques ions (Socías e al., 2016; Roy & Chaudhu i, 2008). The esul s o he s udy by Roy and Chaudhu i (2008) showed ha women ended o a e hei heal h wo se and used ewe heal h se ices – epo edly because o he lowe socioeconomic s a us o women. Sonik e al. (2020) added ha disc imina ion agains women in access o heal hca e was no necessa ily he eason. Women and men simply o en had di e en p e e ences as a as medical ea men is conce ned. Nex o gende , age is ano he signi ican ac o , wi h p e en i e and aes he ic mo i es o medical ea men p e ailing a younge ages. Ano he signi ican ac o co-de e mining he EM_3_2022.indd 20 15.9.2022 13:48:55 21 3, XXV, 2022 Economics a e age numbe o doc o ’s isi s is educa ion (Hoeck e al., 2011). Pucia o e al. (2020) see educa ion as impo an ac o a ec ing an indi idual’s le el o pe cei ed heal h. Acco ding o Czibe e e al. (2019) he le el o he highes a ained educa ion in luence he heal h s a us indi ec ly. They p o ed ha he educa ion ha e signi ican impac only on he age when an illness begun. Pucia o e al. (2020) alks abou a ma i al s a us as a de e minan o heal h condi ions. The ma i al s a us is closely ela ed o he household composi ion. Radulescu e al. (2012) explain ha amily membe s and also iends can in luence an app oach o he indi idual o he heal h. Gende , age and o he demog aphic and socioeconomic ac o s also a ec an app oach o indi iduals o heal h isky beha iou (Mo ke ičius e al., 2020; Kim e al., 2018). Kunzo á and H ubá (2013) poin ou ha heal h is co ela ed wi h many ac o s and also wi h li es yle. Failu e o main ain a heal hy li es yle pu consume s u i ely a isk o ill heal h (Mlčocho á & Papežo á, 2012). The need o heal hca e o medical ea men may no be me due o a a ie y o easons. Kenkel (1990) explained he ela ionship be ween heal hca e and indi iduals’ le el o knowledge and a ailable in o ma ion. Acco ding o his au ho , poo ly in o med consume s ended o unde es ima e he impo ance o heal hca e. Schmid (2015), on he o he hand, ound ha in o ma ion had a nega i e e ec on he use o heal hca e se ices. This was supposedly ela ed o ea s o being examined and diagnosed due o which people did no seek necessa y medical ca e. Acco ding o Fio illo (2020) and Popo ic e al. (2017), he mos common easons o no seeking medical ca e we e inancial and ime cons ain s and he dis ance o heal h acili ies (in connec ion wi h he ‘wai -and-see’ app oach used by he s a o medical acili ies). I mus be aken in o accoun ha mee ing heal h- ela ed needs has a s ongly indi idual dimension and is no a ma e o cou se o all indi iduals. Sa is ac ion a es a e no he same o e e yone unde iden ical condi ions (Ban hin e al., 2008). The e a e plen y o objec i e indica o s ha ell us abou he a ailabili y and quali y o heal hca e in indi idual coun ies. These objec i e da a speak o he heal hca e sys em as a whole in e ms o i s quali y, new me hods, and achie ed esul s. Howe e , he objec i e da a do no add ess how heal hca e se ices p o ided a e pe cei ed by indi iduals, whe he heal hca e is a ailable a he ime and quali y needed, no wha a e he easons o any ailu e o mee he need o heal hca e. The in o ma ion on how indi iduals subjec i ely pe cei e heal h and heal hca e se ices a e o u mos impo ance o any esponsible na ional heal hca e sys em – hence he alue o subjec i e a iables in analyses in his a ea (Schokkae e al., 2017). As explained by Bo iso a (2019), bo h subjec i e and objec i e indica o s o heal h should be used whe e e possible because hey o en in e ac wi h each o he . Heal h policies should adop a mul idimensional app oach and de elop incen i es o emo e ba ie s ha limi consume access o heal h se ices (Popo ic e al., 2017). 2. Resea ch Me hodology To lea n abou he beha iou o indi iduals in ela ion o hei s a e o heal h, he au ho s used da a ob ained wi hin he EU-SILC su ey (Eu opean Union – S a is ics on Income and Li ing Condi ions), speci ically, he EU-SILC 2017. In addi ion, he ex ensi e EU-SILC mic oda a se p o ided de ailed in o ma ion on he income si ua ions o households and indi iduals. The da a also allowed o he iden i ica ion o households and indi iduals in e ms o a ious demog aphic and socioeconomic ac o s, as well as a desc ip ion o households’ and indi iduals’ li ing condi ions in di e en a eas o li e. The EU-SILC su ey is manda o y in all EU coun ies and ollows a uni o m me hodology published by Eu os a (Eu os a , 2019). Eu os a also publishes a uni o m me hodology o u he p ocessing o he esul s. In 2017, EU-SILC was conduc ed in a o al o 256,468 Eu opean households and had a o al o 515,880 indi idual esponden s ( his is he numbe o cases analysed in his pape ). The EU-SILC mic oda a da abase o iginally included 7 indica o s desc ibing subjec i e pe cep ions o esponden s conce ning he need and he a ailabili y o heal hca e se ices. The da abase has been ex ended in 2017 by an ad-hoc module o ano he 7 indica o s desc ibing he inancial demandingness o heal hca e, as pe cei ed subjec i ely by households. This means, o example, he cos o medicines and den al ca e, o he numbe o EM_3_2022.indd 21 15.9.2022 13:48:55 22 2022, XXV, 3 Economics isi s o medical specialis s. The EU-SILC da a con ain a con e sion ac o which is used as a weigh in he con e sion o he sample da a o he base popula ion (i.e., he whole popula ion o he coun y and he whole EU). A i e-poin scale (1 – e y good s a e o heal h; 2 – good; 3 – ai ; 4 – poo ; 5 – e y poo ) was used o subjec i e s a e o heal h assessmen s. The au ho s used clus e analysis o iden i y segmen s o EU ci izens ha showed simila i ies in subjec i e pe cep ions o he s a e o heal h. Subjec i e assessmen o heal h e alua ed by consume s is he a iable applied in he clus e analysis. The clus e s a e o med acco ding o he p opo ion o indi iduals among esponden s in each coun y who a e hei heal h as e y good, good, ai , poo and e y poo . The goal o clus e analysis is o classi y objec s in o a ce ain numbe o clus e s. Objec s wi hin a clus e a e simila o he g ea es ex en possible and objec s wi hin a clus e a e he leas possibly simila o objec s om o he clus e s. Indi idual objec s a e g adually g ouped in o smalle clus e s and hese clus e s a e hen me ged o o m la ge clus e s (Meloun & Mili ký, 2012). The au ho s used he K-means algo i hm which iden i ies homogeneous g oups o esea ch objec s based on selec ed cha ac e is ics. Fo each o he ini ial clus e s, he au ho s de e mined he cen oid alue (cen oid is a ec o o he a e age alues o each a iable). Objec s we e assigned o clus e s based on he cen oid o which he objec was closes . The op imal numbe o clus e s is e i ied by applying ANOVA analysis showing signi ican di e ence be ween clus e s. Acco ding o Hebák e al. (2015), K-means algo i hm is an i e a i e p ocedu e ha minimizes he unc ion o he ollowing o mula (1): , (1) whe e he uih ∈ {0,1} elemen s indica e whe he he i- h objec belongs ( alue 1) o does no belong ( alue 0) o he h- h clus e and is a ec o o a e age alues o he h- h clus e . The condi ions o he ollowing o mula (2) mus be me : (2) The chances o mee ing he need o heal hca e wi h espec o di e en ca ego ies o demog aphic and socioeconomic ac o s ha e been assessed by logis ic eg ession analysis. The explained a iable could ake wo alues: unme need o heal hca e (0) and me need o heal hca e (1). The ollowing ac o s we e used as explana o y a iables: gende , educa ion, economic s a us, sec o o economic ac i i y, and household income g oup. The bina y logis ic eg ession model can be exp essed by he o mula (3) showing he ela ionship be ween he p obabili y o a phenomenon P(x) (Y = 1), i.e., mee ing he need o heal hca e, unde condi ions gi en by he alues o he independen a iables (x): . (3) The ln (P/(1 – P)) o mula (called he logi o P), can be exp essed as a weigh ed sum o he alues o he independen a iables. The logi o P is he loga i hm o he p obabili y o occu ence o he phenomenon unde s udy. The model can be also exp essed by he ollowing o mula (4): , (4) whe e he pa ame e es ima es βi a e ob ained om he measu emen ma ix o x. I βi is equal o ze o, hen he pa ame e has no e ec on he obse ed phenomenon (Hendl, 2006). The quali y o he bina y eg ession model is assessed by he Nagelke ke R-squa ed indica o , he signi icance o he model is e i ied by he Hosme and Lemeshow es . The VIF indica o is used o e i y a p esence o mul icollinea i y in models. The VIF alues highe han 10 indica es mul icollinea i y in he model (Hebák e al., 2015). The EU-SILC da a ha e been p ocessed by he IBM SPSS S a is ics so wa e. The algo i hm o clus e analysis and he bina y logis ic eg ession ha e also been implemen ed in he SPSS so wa e. 3. Resea ch Resul s The au ho s ook he oppo uni y o analyse da a om he EU-SILC su ey conduc ed in 2017. In ha yea , he su ey was ex ended by an ad hoc module aimed a heal hca e. The esponden s commen ed on how hey subjec i ely pe cei ed EM_3_2022.indd 22 15.9.2022 13:48:56 23 3, XXV, 2022 Economics hei s a es o heal h and whe he hei heal hca e needs we e me . I a esponden said hei need o heal hca e was no me , hey we e asked o gi e he easons. The esul s o he su ey p o ide impo an in o ma ion on heal h- ela ed beha iou o people and, gi en he ep esen a i eness o he popula ion, a e e y use ul o he implemen a ion o co ec i e measu es in he heal h sec o . Gi en he size o he su ey sample (co e ing 27 coun ies, i.e., abou 515 housand EU esponden s and dozens o con en ques ions), his pape could no co e all he alues included in he su ey, ins ead, he au ho s ocused on ypical and ex eme alues only. 3.1 Indi idual Pe cep ions o he S a e o Heal h The esul s o he subjec i e assessmen s o he s a e o heal h showed ha he e we e coun ies whe e almos 50% o esponden s a ed hei s a es o heal h as ‘ e y good’ ( o example Cyp us and G eece). In mos coun ies, a majo pa o esponden s e alua ed hei s a es o heal h by he g ade o ‘2’, i.e., ‘good’ ( epo ed by abou 50% o esponden s), o g ade ‘3’ – ‘ ai ’ (20–30% o esponden s). Howe e , he e we e coun ies whe e some esponden s (up o 10% o in he o de o ens o %) a ed hei s a es o heal h as ‘ e y poo ’ o ‘poo ’. The highes equencies o nega i e heal h e alua ions we e ound in he ollowing coun ies (Tab. 1). In coun ies wi h nega i e heal h a ings (see Tab. 1), esponden s also mo e equen ly epo ed issues ela ed o long- e m illnesses ha limi ed hei e e yday ac i i ies. In o he EU coun ies ( hose no lis ed in Tab. 1), 2% o ewe esponden s assessed hei s a es o heal h as e y poo . To p o ide an o e all o e iew and summa i- ze he subjec i e pe cep ion o he s a e o heal h in all EU coun ies, he au ho s employed clus e analysis and he K-means algo i hm. As a esul , h ee clus e s o indi iduals we e iden i ied based on he pe cei ed s a uses o heal h. The p opo ions o indi iduals e alua ing hei heal h s a us as e y good, good, ai , poo and e y poo enabled he o ma ion o h ee segmen s and so ed coun ies in o segmen s acco ding assessmen s by esiden s’ ep esen a i es (Tab. 2). C oa ia Po ugal Hunga y La ia Li huania Poland Bulga ia Ve y poo s a e o heal h 3.9% 3.6% 3.2% 3.1% 3.1% 2.7% 2.5% Poo s a e o heal h 14.0% 11.0% 9.0% 13.8% 13.0% 10.0% 8.0% Sou ce: EU-SILC mic oda a (Eu os a , 2021), own using IBM SPSS S a is ics Tab. 1: EU coun ies wi h he highes p opo ions o indi iduals pe cei ing nega i ely hei s a es o heal h Clus e 1 ‘op imis ic’ Clus e 2 ‘neu al’ Clus e 3 ‘pessimis ic’ EU coun ies in he clus e Aus ia, Cyp us, G eece, C oa ia, I eland Belgium, Bulga ia, Ge many, Denma k, Spain, Finland, F ance, I aly, Luxembou g, Mal a, Ne he lands, Romania, Sweden, Slo akia Czech Republic, Es onia, Hunga y, Li huania, La ia, Poland, Po ugal, Slo enia Ve y good SH 40% 23% 13% Good SH 33% 48% 41% Fai SH 18% 21% 32% Poo SH 7% 6% 11% Ve y poo SH 2% 2% 3% Sou ce: EU-SILC mic oda a (Eu os a , 2021), own using IBM SPSS S a is ics Tab. 2: Subjec i e assessmen s o heal h (SH) in EU coun ies EM_3_2022.indd 23 15.9.2022 13:48:56 24 2022, XXV, 3 Economics Subsequen ly, ANOVA analysis con i med he co ec numbe o clus e s iden i ied (Tab. 3). Signi icance alues a e below he signi icance le el α = 0.05. Clus e s a e signi ican ly di e en . Also, he e was a ze o change acco ding o he i e a ion his o y a e h ee i e a ions du ing K-means algo i hm p ocess. I mo e clus e we e o med, he di e ence be ween clus e s was no con i med (signi icance alues we e abo e he signi icance le el). The K-means algo i hm assigned indi iduals om i e coun ies o he i s clus e , o which almos h ee qua e s a ed hei heal h as good and 40% as e y good. Due o he posi i e heal h assessmen s, he clus e has been named as ‘op imis ic’. In he second g oup, abou hal o he esponden s a ed hei heal h as good. The second segmen included he la ges numbe o EU coun ies compa ed o he o he segmen s. The hi d g oup has been mo e pessimis ic abou hei heal h, wi h a highe numbe o esponden s a ing hei heal h as ai . On a e age, 14% o esponden s in his g oup e alua ed hei heal h as poo . Responden s om coun ies wi h mo e nega i e a ings we e mo e likely o epo p oblems ela ed o long- e m illness o heal h limi a ions. The a e age sha e o a coun y’s popula ion epo ing limi a ions in e e yday ac i i ies due o poo heal h ha e amoun ed o uni s o pe cen . Ye , he e we e coun ies whe e people did no pe cei e such limi a ions a all (Spain, I eland, Mal a, and Sweden). Howe e , when d awing hese conclusions, we need o ake in o accoun whe he he condi ions c ea ed by he s a e a e so sa is ac o y ha people can lead ac i e li es wi hou limi a ions, o whe he he epo ed opinions we e shaped by low awa eness o he possibili ies o imp o ing li ing condi ions. 3.2 Pe cei edFul ilmen o Heal hca e Needs When asked whe he he medical assis ance eques ed was ac ually ecei ed, he e we e coun ies whe e almos 100% o esponden s answe ed posi i ely. These we e, o example, Spain, Aus ia, Mal a, and Luxembou g. In some coun ies, on he o he hand, signi ican amoun s o esponden s answe ed nega i ely, i.e., ha hey did no ecei e he ea men hey needed. In G eece, o example, 25% o esponden s ga e nega i e answe s, in Es onia, i was 13% o esponden s, in Poland 12%, and in La ia 10%. In o he EU coun ies, unme heal hca e needs we e epo ed by up o 10% o esponden s. The au ho s used bina y logis ic eg ession o ind ou which ac o s in luenced he ul ilmen o he need o heal hca e and which ca ego ies o demog aphic and socioeconomic ac o s inc eased he chances o he ul ilmen o he need. The explained a iable in he model has been he ul ilmen o he need o heal hca e. The a iable could ake wo alues: 0 indica ing no sa is ac ion o he need ( ailu e o mee he need o heal hca e); and 1 indica ing sa is ac ion o he need. The explana o y a iables en e ing he eg ession model we e Gende , Educa ion, Household composi ion, Economic ac i i y, Income quin ile based on he household’s disposable income, and Sec o o economic ac i i y based on ISCO (In e na ional S anda d Classi ica ion o Occupa ions). The au ho s ha e calcula ed he bina y logis ic eg ession o all h ee segmen s (c ea ed based on he subjec i e assessmen s o heal h by he esponden s – see Tab. 2). This allowed o explana ions o he esul s o bina y logis ic eg essions in ela ion o op imis ic and Clus e E o F Sig. Mean squa e d Mean squa e d Ve y good SH 1,124.076 236.397 24 30.884 0.000 Good SH 428.770 222.651 24 18.930 0.000 Fai SH 415.434 215.236 24 27.267 0.000 Poo SH 66.515 25.447 24 12.211 0.000 Ve y poo SH 3.953 20.626 24 6.315 0.006 Sou ce: EU-SILC mic oda a (Eu os a , 2021), own using IBM SPSS S a is ics Tab. 3: ANOVA in he clus e analysis EM_3_2022.indd 24 15.9.2022 13:48:57 25 3, XXV, 2022 Economics pessimis ic assessmen s o heal h by indi idual esponden s. Be o e in e p e ing he model, he p esence o mul icollinea i y in h ee models o all segmen s was e i ied. The linea eg ession p ocedu e wi h same p edic o s was used o his pu pose and collinea i y diagnos ics we e eques ed. All alues o VIF indica o s a e below he alue 10 (Tab. 4). Mul icollinea i y is no p esen in he models, as indica ed by he low alues o he Condi ion indexes implemen ed in he IBM SPSS S a is ics. In all h ee logis ic eg essions, Hosme and Lemeshow es s we e used o p o e he signi icance o he models ( he esul ing p- alues had o be lowe han 0.05). The Nagelke ke R-squa ed indica o s p o ed he quali y o he models as 83% o he a iabili y in he dependen a iable was explained o he i s segmen (Tab. 5), o he second model (Tab. 6) i was 89%, and o he hi d model (Tab. 7) i was 73% o he a iabili y o he dependen a iable. Ca ego ies wi h he highes chances o mee ing he heal h o heal hca e ha e been highligh ed in bold in he ables. The esul s o he bina y logis ic eg ession o he i s segmen (Tab. 5) showed ha all he explained a iables in luenced he ul ilmen o he need o heal hca e. The s onges in luen- ce has been iden i ied in he ‘Sec o ’ a iable, Segmen 1 VIF Segmen 2 VIF Segmen 3 VIF Gende 9.446 8.677 9.347 Household composi ion 4.994 4.733 5.349 Educa ion 9.654 9.847 9.822 Economic ac i i y 3.549 3.585 3.705 Quin iles 5.791 6.041 6.184 Sec o 4.496 5.096 5.435 Sou ce: EU-SILC mic oda a (Eu os a , 2021), own using IBM SPSS S a is ics Tab. 4: Collinea i y s a is ics Es ima e B S anda d de ia ion Wald d Sig. Exp(B) Gende (males) 0.074 0.003 863.334 1 0.000 1.077 Household composi ion (o he )a 30,666.113 4 0.000 Household composi ion (single) 0.024 0.003 48.946 1 0.000 1.024 Household composi ion ( wo adul s) 0.433 0.003 20,608.297 1 0.000 1.542 Household composi ion (single pa en ) 0.228 0.009 692.013 1 0.000 1.256 Household composi ion ( wo adul s and child en) 0.378 0.004 10,978.219 1 0.000 1.460 Educa ion (uni e si y)a 138,956.161 20.000 Educa ion (basic) −0.382 0.004 11,072.918 1 0.000 0.682 Educa ion (seconda y/high school) 0.681 0.003 52,078.088 1 0.000 1.976 Tab. 5: Chances o mee ing he need o heal hca e o segmen 1 – ‘op imis ic’ – Pa 1 EM_3_2022.indd 25 15.9.2022 13:48:57 26 2022, XXV, 3 Economics whe e legisla o s and execu i es we e 11 imes mo e likely o ha e hei heal hca e needs me han c a smen and wo ke s. The second mos impo an a iable in e ms o in luence signi i- cance was Economic ac i i y, whe e employees we e ound o ha e he highes chances o he ul ilmen o hei need o heal hca e. Simila ly, he esul s o he logis ic eg ession o he second segmen da a (Tab. 6) p o ed he signi icance o mos o he ac o ca ego ies excep o he ca ego y o ag icul u e in he Sec o a iable and he ca ego y o single pa en s in he Household composi ion a iable. The mos signi ican ac o in e ms Es ima e B S anda d de ia ion Wald d Sig. Exp(B) Economic ac i i y (o he )a 152,401.000 4 0.000 Economic ac i i y (employed) 1.225 0.004 115,136.806 1 0.000 3.403 Economic ac i i y (sel -employed) 0.510 0.005 11,126.210 1 0.000 1.666 Economic ac i i y (unemployed) −0.035 0.004 66.343 1 0.000 0.965 Economic ac i i y (old-age pensione ) 0.669 0.003 36,924.689 1 0.000 1.952 Quin iles ( i h)a 27,200.371 4 0.000 Quin iles ( i s ) 0.467 0.004 16,704.470 1 0.000 1.595 Quin iles (second) 0.460 0.004 17,002.700 1 0.000 1.584 Quin iles ( hi d) 0.411 0.004 13,717.567 1 0.000 1.508 Quin iles ( ou h) 0.436 0.004 14,494.801 1 0.000 1.546 Sec o (c a smen and wo ke s)a 358,990.333 10 0.000 Sec o (legisla o s and execu i es) 2.430 0.006 158,988.056 1 0.000 11.354 Sec o (science and echnology) 1.892 0.007 68,520.486 1 0.000 6.630 Sec o (heal hca e) 1.816 0.008 46,283.147 1 0.000 6.147 Sec o (educa ion and aining) 2.118 0.008 67,896.034 1 0.000 8.311 Sec o (public adminis a ion) 2.051 0.007 83,857.200 1 0.000 7.777 Sec o (in o ma ion echnology) 1.840 0.012 23,631.131 1 0.000 6.293 Sec o (law, cul u e, spo ) 1.445 0.008 33,479.194 1 0.000 4.243 Sec o (o icials) 1.385 0.005 79,697.080 1 0.000 3.995 Sec o (se ices and sales) 0.986 0.003 95,813.287 1 0.000 2.681 Sec o (ag icul u e, o es y, ishing) 0.488 0.003 20,737.801 1 0.000 1.630 Sou ce: EU-SILC mic oda a (Eu os a , 2021), own using IBM SPSS S a is ics No e: a This pa ame e has been se o ze o because i is edundan . Tab. 5: Chances o mee ing he need o heal hca e o segmen 1 – ‘op imis ic’ – Pa 2 EM_3_2022.indd 26 15.9.2022 13:48:58 27 3, XXV, 2022 Economics o inc easing he likelihood o ul illing he need o heal hca e has been he Sec o a iable again, whe e he employees in he heal hca e sec o , he educa ion and aining sec o had he highes chances o ha ing hei heal hca e needs me . The chances o bo h ca ego ies we e almos 7 imes highe compa ed o c a smen and wo ke s. The esul s ha e also shown ha people wi h p ima y educa ion had he highes chances o he ul ilmen o hei heal hca e needs (e en h ee imes highe compa ed o uni e si y g adua es). This inding may be ela ed o he ac ha p ima y educa ion (as he highes le el o educa ion a ained) was epo ed la gely by elde ly esponden s who we e no longe economically ac i e and had su icien ime o heal hca e. Ac ually, ime cons ain s we e one o he main easons o no ul illing he need o heal hca e. I is wo h no ing ha he lowes chances we e iden i ied in he g oups o single mo he s and single households ( he Household composi ion a iable), in he i s segmen (Tab. 5). Es ima e B S anda d de ia ion Wald d Sig. Exp(B) Gende (males) 0.452 0.001 215,965.723 1 0.000 1.571 Household composi ion (o he )a 462,436.433 4 0.000 Household composi ion (single) 0.132 0.001 11,210.770 1 0.000 1.141 Household composi ion ( wo adul s) 0.620 0.001 262,190.359 1 0.000 1.859 Household composi ion (single pa en ) 0.003 0.003 1.207 1 0.272 1.003 Household composi ion ( wo adul s and child en) 0.690 0.001 255,848.438 1 0.000 1.994 Educa ion (uni e si y)a 868,163.355 20.000 Educa ion (basic) 1.194 0.002 542,042.619 1 0.000 3.299 Educa ion (seconda y/high school) 0.929 0.001 755,609.824 1 0.000 2.531 Economic ac i i y (o he )a 1,174,238.320 4 0.000 Economic ac i i y (employed) 1.204 0.001 1,026,477.356 1 0.000 3.333 Economic ac i i y (sel -employed) 1.075 0.002 287,131.505 1 0.000 2.931 Economic ac i i y (unemployed) 0.546 0.002 102,352.112 1 0.000 1.726 Economic ac i i y (old-age pensione ) 1.009 0.001 588,650.365 1 0.000 2.744 Quin iles ( i h)a 320,935.300 4 0.000 Quin iles ( i s ) 0.056 0.001 1,740.229 1 0.000 1.057 Quin iles (second) 0.361 0.001 69,696.199 1 0.000 1.434 Quin iles ( hi d) 0.615 0.001 186,094.038 1 0.000 1.850 Quin iles ( ou h) 0.554 0.001 150,418.704 1 0.000 1.741 Tab. 6: Chances o mee ing he need o heal hca e o segmen 2 – ‘neu al’ – Pa 1 EM_3_2022.indd 27 15.9.2022 13:48:59 34 2022, XXV, 3 Economics S a us o he Unemployed. 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