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Background: The objective of this paper is to explore unmet health care needs in Hungary in ambulatory care due to costs and difficulties in travelling, and to analyze how unmet needs relate to socio-demographic characteristics.Methods: The quantitative analysis is based on a national, representative online survey carried out in Hungary on a sample of 1000 respondents in early 2019 using a proposed set of questions developed by the OECD. We present and compare unmet medical needs in different socio-demographic groups, and we use multivariate logistic regression analysis to identify the main determinants of unmet medical needs.Results: Among responders who had medical problems in the last 12 months, 27.3% reported forgone medical visit due to difficulties in travelling, 24.2% had unfilled prescription for medicine due to costs, 21.4% reported forgone medical visit or follow-up visit due to costs and 16.6% reported skipped medical test, treatment or other follow-up due to costs. These shares are much higher than presented previously in international databases. The logistic model indicates that respondents were significantly more likely to report unmet needs if they were women, younger or belonged to first and second income quintiles.Conclusions: Policy makers need to address the issue of high prevalence of forgone medical care among the Hungarian population to avoid deterioration of population health and inequalities in access. As a first step, policies should try to decrease financial burden of vulnerable groups to improve access. [ABSTRACT FROM AUTHOR]

Background: EQ-5D-3L population data are available only from Hungary, Poland and Slovenia in Central and Eastern Europe (CEE). We aimed to compare the accessible studies and estimate a regional EQ-5D-3L population norm for CEE.Methods: A combined dataset using patient-level data of 8850 respondents was created. Based on the European Census of 2011, regional population norm estimates were calibrated by gender, age and education for the joint citizenry of 11 CEE countries.Results: EQ-5D-3L health states were available for 6926 and EQ VAS scores for 6569 respondents. Demographic characteristics of the samples reflected the recruitment methods (Hungary: online; Slovenia: postal survey, Poland: personal interviews). Occurrence of problems differed significantly by educational level in all the five dimensions (p < 0.001). The inter-country differences persisted after controlling for demographic variables. The estimated EQ-5D-3L index CEE norms with UK tariffs for age groups 18-24, 25-34, 35-44, 45-54, 55-64, 65-74 and 75 + were 0.911, 0.912, 0.871, 0.817, 0.762, 0.743 and 0.636 for males and 0.908, 0.888, 0.867, 0.788, 0.752, 0.68 and 0.584 for females, respectively. Estimates were provided also using Polish, European and Slovenian value sets.Conclusions: Besides gender and age, education should be considered during the design and interpretation of quality-of-life studies in CEE. The estimated regional EQ-5D-3L population norm may be used as a benchmark by CEE countries with lack of local dataset. However, the substantial inter-country differences in health status and scarcity of data over age 65 call for harmonized country-specific EQ-5D-3L population norm studies in the CEE region. [ABSTRACT FROM AUTHOR]

Background: We adapted the eHealth Literacy Scale (eHEALS) for Hungary and tested its psychometric properties on a large representative online sample of the general population.Methods: The Hungarian version of eHEALS was developed using forward-backward translation. For the valuation study, 1000 respondents were recruited in early 2019 from a large online panel by a survey company. We tested internal consistency, test-retest reliability and construct and criterion validity using classical test theory, as well as item characteristics using an item-response theory (IRT) graded response model (GRM).Results: 55% of respondents were female, and 22.1% were ≥ 65 years old. Mean eHEALS score was 29.2 (SD: 5.18). Internal consistency was good (Cronbach's α = 0.90), and test-retest reliability was moderate (intraclass correlation r = 0.64). We identified a single-factor structure by exploratory factor analysis, explaining 85% of test variance. Essential criteria for GRM analysis were met. Items 3 and 4 (search of health resources) were the least difficult, followed by items 5 and 8 (utilisation of health information), and then items 1 and 2 (awareness of health resources). Items 6 and 7 (appraisal of health resources) were most difficult. The measurement properties of eHEALS were not affected by gender, age, education or income levels. Female gender, older age, intensity of health information seeking, formal health education and visit at the electronic health-record website were associated with higher eHEALS scores, as well as best and worst self-perceived health states, BMI < 25 and participation at health screenings over the past year.Conclusions: The Hungarian eHEALS is a useful and valid tool for measuring subjective eHealth literacy. [ABSTRACT FROM AUTHOR]