Unequal burden of COVID-19 in Hungary: a geographical and socioeconomic analysis of the second wave of the pandemic

Abstract

IntroductionWe describe COVID-19 morbidity, mortality, case fatality and excess death in a country-wide study of municipalities in Hungary, exploring the association with socioeconomic status.MethodsThe spatial distribution of morbidity, mortality and case fatality was mapped using hierarchical Bayesian smoothed indirectly standardised ratios. Indirectly standardised ratios were used to evaluate the association between deprivation and the outcome measures. We looked separately at morbidity and mortality in the 10 districts with the highest and 10 districts with the lowest share of Roma population.ResultsCompared with the national average, the relative incidence of cases was 30%–36% lower in the most deprived quintile but the relative mortality and case fatality were 27%–32% higher. Expressed as incidence ratios relative to the national average, the most deprived municipalities had a relative incidence ratio of 0.64 (CI: 0.62 to 0.65) and 0.70 (CI: 0.69 to 0.72) for males and females, respectively. The corresponding figures for mortality were 1.32 (CI: 1.20 to 1.44) for males and 1.27 (CI: 1.16 to 1.39) for females and for case fatality 1.27 (CI: 1.16 to 1.39) and 1.32 (CI: 1.20 to 1.44) for males and females, respectively. The excess death rate (per 100 000) increased with deprivation levels (least deprived: 114.12 (CI: 108.60 to 119.84) and most deprived: 158.07 (CI: 149.30 to 167.23)). The 10 districts where Roma formed the greatest share of the population had an excess mortality rate 17.46% higher than the average for the most deprived quintile.ConclusionsThose living in more deprived municipalities had a lower risk of being identified as a confirmed COVID-19 case but had a higher risk of death. An inverse association between trends in morbidity and mortality by socioeconomic conditions should be a cause for concern and points to the need for responses, including those involving vaccination, to pay particular attention to inequalities and their causes.

Narrowing gap in regional and age-specific excess mortality in the first year and a half of COVID-19 in Hungary

“…This is in line with the results ofOroszi et al (2021).14 Although the age structure of communities influences the impact of the pandemic, regional differences in excess mortality cannot be fully explained by the different demographic characteristics of the regions. Although the proportion of people aged 65+ is lowest in the Northern Great Plain, where the excess mortality was relatively low compared with other regions, the proportion of older people in Hungary is highest in the Southern Great Plain and South Transdanubia.…”

“…One of the most important questions to address when protecting against COVID-19 is what factors influence the spread of the pandemic and how these factors change during the different phases of the pandemic. An important starting point is to map the geographical characteristics of the pandemic (Oroszi et al, 2021, Uzzoli et al, 2021a. In this study, we relied on regional level (NUTS-2) analysis, working with relatively few but large geographical units.…”

“…The geographical spread of the pandemic has also been given particular attention. While some of these studies have compared the domestic experience with that of other countries (Kovalcsik et al, 2021), most have focused on geographical differences within countries (Oroszi et al, 2021;Uzzoli et al, 2021a;Uzzoli et al, 2021b).…”

Effect of the COVID-19 Pandemic on Cost-Related Prescription Nonredemption, Health care Seeking and Hospitalization in Hungary: A Nationally Representative Cross-Sectional Study

“…Another study revealed that sociodemographic inequity highly determined the impact of the pandemic in Hungary. The most deprived settings had a lower incidence of morbidity with higher mortality and case fatality rates [37]. Nonetheless, we did not find any investigation of the dynamics of fundamental health care service utilization attributed to the pandemic lockdown in Hungary.…”

Unmet Health Need Among Roma: Visual Acuity and the Use of Vision Correcting Aids in the Hungarian Roma Population

“…Most of the European Union`s Roma population lives in Eastern-European countries including Romania, Bulgaria, Hungary and Slovakia where they make up between 5 and 10 per cent of the population, but there are also sizeable Roma minorities in the Western Balkan countries, as well as in Spain, Italy, France and the UK [2][3][4] . In comparison with the general populations, Roma populations have poorer health and high burden of both communicable and non-communicable diseases [5][6][7][8][9][10][11][12][13] , which can be attributed to social exclusion negatively in uencing their access to educational, social and health services 14,15 .…”