Az epidemiológiai surveillance és járványmatematikai előrejelzések szerepe a pandémiás hullámok megelőzésében, mérséklésében – hol tartunk most, és hová kellene eljutni

A Járványmatematikai és Epidemiológiai Projekt egy egyedülálló kezdeményezés Magyarországon, mely jelentős tudást és tapasztalatot halmozott fel a COVID–19 világjárvány során. Jelen tanulmány a pandémia 2. hullámának példáján keresztül áttekinti, hogy miként működött a járványügyi észlelés és monitorozás rendszere, hogyan, milyen eredményekkel végezték a projekt munkatársai a helyzet- és kockázatértékelést, az előrejelzések készítését, végül javaslatokat fogalmaz meg a surveillance- és előrejelző rendszer fejlesztésére a járványügyi biztonság növelése érdekében.A 2. járványhullám 2020. június 22. és 2021. január 24. között zajlott Magyarországon, melynek során a megerősített COVID–19 esetek száma 356 197 fő volt, ami az első hullámban regisztrált esetszám 87-szerese. Összesen 12 226 megerősített COVID–19 halálesetet regisztráltak, az első hullámban jelentett szám 21-szeresét. Az országos R érték először 2020 augusztusában emelkedett 1,0 fölé. Mintegy 3 héttel azután, hogy az R érték augusztus utolsó hetében tartósan 1,0 fölé emelkedett, a halálos kimenetelű COVID–19 esetszámok növekedése is elindult, mivel a fiatalokról a járvány az idősebb korosztályokra is átterjedt. Mindezt a matematikai modellezési eredmények hetekkel korábban jelezték. November elején az előrejelzés 12 000 fő feletti kórházi ápoltat vetített előre a karácsonyi időszakra, melynek el-kerülésére kormányzati intézkedéscsomag készült. A 2020. november 11-i szigorítás a járványt az eredeti pályáról eltérítette, így a kórházban kezeltek száma a 2. hullámban az előrejelzésnek megfelelően 8018 főnél elérte a csúcsot, majd csökkenni kezdett. Január elején a modellezés azt mutatta, hogy a lecsengő szakaszban, az akkori intézkedések mellett is képes az időközben hazánkban is megjelent új variáns, a gyorsabban terjedő SARS-CoV-2 B.1.1.7, járvány-ügyi fordulatot hozni, ami szintén megvalósult. Összességében az epidemiológiai helyzetértékelés és matematikai modellezés képes volt a második hullám minden fő aspektusát időben és jól megragadni, a veszélyes folyamatokat előre jelezni és ezzel lehetőséget adni a gyors reagálásra. A 2. hullám tapasztalatai megmutatták, hogy a járványmatematikai és epidemiológiai képességek milyen hozzá-adott értékkel bírnak a döntéstámogatásban. Az észlelési és előrejelzési rendszereink megerősítése és a matematikai modellezéssel egységes keretrendszerben történő továbbfejlesztése további lehetőségeket nyithat meg az észlelés, megelőzés, egészségügyi és gazdasági károk elhárítása érdekében szükséges döntési folyamatok bizonyítékalapú támogatásában, és az ország járványügyi biztonságának növelésében

Real-time monitoring of the effectiveness of six COVID-19 vaccines against laboratory confirmed COVID-19 in Hungary in 2021 using the screening method

Several studies have reported the waning effectiveness of COVID-19 vaccines. This study aims to demonstrate the applicability of the screening method for estimating vaccine effectiveness (VE) in a pandemic. We report VE in Hungary, estimated with the screening method, in 2021, covering a period of Alpha and the Delta variant, including the booster dose roll-out. Hungary is in a unique position to use six different vaccines in the same population. All vaccines provided a high level of protection initially, which declined over time. While the picture is different in each age group, the waning of immunity is apparent for all vaccines, especially in the younger age groups and the Sinopharm, Sputnik-V, and AstraZeneca vaccines, which performed similarly. This is clearly reversed by booster doses, more prominent for those three vaccines, where the decline in protection is more evident. Overall, two vaccines, Pfizer/BioNTech and Moderna, tend to produce the best results in all age groups, even with waning immunity considered. Using the screening method in future pandemic waves is worthwhile, especially in countries struggling with a lack of resources or when there is a need to deliver VE results within a short timeframe due to urgent decision-making

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

INTRODUCTION: We 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. METHODS: The 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. RESULTS: Compared 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. CONCLUSIONS: Those 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

Characteristics of the Third COVID-19 Pandemic Wave with Special Focus on Socioeconomic Inequalities in Morbidity, Mortality and the Uptake of COVID-19 Vaccination in Hungary.

Governments are increasingly looking to vaccination to provide a path out of the COVID-19 pandemic. Hungary offers an example to investigate whether social inequalities compromise what a successful vaccine program can achieve. COVID-19 morbidity, mortality, and vaccination coverage were characterized by calculation of indirectly standardized ratios in the Hungarian population during the third pandemic wave at the level of municipalities, classified into deprivation quintiles. Then, their association with socioeconomic deprivation was assessed using ecological regression. Compared to the national average, people living in the most deprived municipalities had a 15-24% lower relative incidence of confirmed COVID-19 cases, but a 17-37% higher relative mortality and a 38% lower vaccination coverage. At an ecological level, COVID-19 mortality showed a strong positive association with deprivation and an inverse association with vaccination coverage (RRVaccination = 0.86 (0.75-0.98)), but the latter became non-significant after adjustment for deprivation (RRVaccination = 0.95 (0.84-1.09), RRDeprivation = 1.10 (1.07-1.14)). Even what is widely viewed as one of the more successful vaccine roll outs was unable to close the gap in COVID-19 mortality during the third pandemic wave in Hungary. This is likely to be due to the challenges of reaching those living in the most deprived municipalities who experienced the highest mortality rates during the third wave

Estimates of Pandemic Influenza Vaccine Effectiveness in Europe, 2009–2010: Results of Influenza Monitoring Vaccine Effectiveness in Europe (I-MOVE) Multicentre Case-Control Study

Results from a European multicentre case-control study reported by Marta Valenciano and colleagues suggest good protection by the pandemic monovalent H1N1 vaccine against pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine on H1N1

I-MOVE Multi-Centre Case Control Study 2010-11: Overall and Stratified Estimates of Influenza Vaccine Effectiveness in Europe

BACKGROUND: In the third season of I-MOVE (Influenza Monitoring Vaccine Effectiveness in Europe), we undertook a multicentre case-control study based on sentinel practitioner surveillance networks in eight European Union (EU) member states to estimate 2010/11 influenza vaccine effectiveness (VE) against medically-attended influenza-like illness (ILI) laboratory-confirmed as influenza. METHODS: Using systematic sampling, practitioners swabbed ILI/ARI patients within seven days of symptom onset. We compared influenza-positive to influenza laboratory-negative patients among those meeting the EU ILI case definition. A valid vaccination corresponded to > 14 days between receiving a dose of vaccine and symptom onset. We used multiple imputation with chained equations to estimate missing values. Using logistic regression with study as fixed effect we calculated influenza VE adjusting for potential confounders. We estimated influenza VE overall, by influenza type, age group and among the target group for vaccination. RESULTS: We included 2019 cases and 2391 controls in the analysis. Adjusted VE was 52% (95% CI 30-67) overall (N = 4410), 55% (95% CI 29-72) against A(H1N1) and 50% (95% CI 14-71) against influenza B. Adjusted VE against all influenza subtypes was 66% (95% CI 15-86), 41% (95% CI -3-66) and 60% (95% CI 17-81) among those aged 0-14, 15-59 and ≥60 respectively. Among target groups for vaccination (N = 1004), VE was 56% (95% CI 34-71) overall, 59% (95% CI 32-75) against A(H1N1) and 63% (95% CI 31-81) against influenza B. CONCLUSIONS: Results suggest moderate protection from 2010-11 trivalent influenza vaccines against medically-attended ILI laboratory-confirmed as influenza across Europe. Adjusted and stratified influenza VE estimates are possible with the large sample size of this multi-centre case-control. I-MOVE shows how a network can provide precise summary VE measures across Europe

I-MOVE multicentre case–control study 2010/11 to 2014/15 : is there within-season waning of influenza type/subtype vaccine effectiveness with increasing time since vaccination?

Influenza vaccines are currently the best method available to prevent seasonal influenza infection. In most European countries one dose (or two doses for children) of seasonal vaccine is given from September to December to the elderly and other target groups for vaccination. In Europe, influenza seasons can last until mid-May (1), and it is expected that vaccination conveys protection on the individual for the duration of the season. In 13/15 reviewed studies on the length of vaccine-induced protection among the elderly, using anti-haemagglutination antibody titres as a proxy for seroprotection levels, seroprotection rates lasted at least >4 months after vaccination (2). However in the 2011-12 influenza season various studies in Europe reported a decrease in influenza vaccine effectiveness (VE) against A(H3N2) over time within the season (3–5). In the United States, a decrease in VE against A(H3N2) with time since vaccination was suggested in the 2007-8 influenza season (6). The observed decrease of VE over time can be explained by viral change (notably antigenic drift) occurring in the season. Drift in B viruses may be slower than in A viruses (7), and A(H3N2) viruses undergo antigenic drift more frequently than A(H1N1)pdm09 viruses (8). The decrease of VE over time can also be explained by a waning of the immunity conferred by the vaccine independently from viral changes. If vaccine-induced protection wanes more rapidly during the season, then depending on the start and duration of the influenza season, the decline of VE may cause increases in overall incidence, hospitalisations and deaths. Changes to vaccination strategies (timing and boosters) may be needed. As anti-haemagglutination antibody titres are not well defined as a correlate of protection (9,10), vaccine efficacy (as measured in trials) or vaccine effectiveness observational studies may be one way to measure vaccine-induced protection. These studies require a large sample size to model VE by time since vaccination and currently, most of the seasonal observational studies lack the precision required to provide evidence for waning immunity. In this study we pooled data across five post-pandemic seasons (2010/11-2014/15) from the I-MOVE (Influenza - Monitoring Vaccine Effectiveness) multicentre case control studies (1,3,11,12), to obtain a greater sample size to study the effects of time since vaccination on influenza type/subtype-specific VE. We measure influenza type/subtype-specific VE by time since vaccination for the overall season, but also in the early influenza phase; under the hypothesis that virological changes are fewer in the early season, but waning of the vaccine effect should be present regardless of time within the influenza phase