Long-term impact of unhealthy food tax on consumption and the drivers behind: A longitudinal study in Hungary.

Several countries have introduced public health product taxes with the objective of reducing the absolute amount of consumption of unhealthy food and tackling obesity. This study aims to estimate the long-term impact of the Hungarian public health product tax introduced in 2011. To achieve this, a unique consumer purchase dataset was analysed to examine daily fast-moving consumer goods purchases from a representative sample of 2,000 households from 2010 to 2018. The results indicate that the tax has been fully reflected in consumer prices. A decline in consumption was observed initially, consistent with previous experiences in Hungary and other countries. However, over time, the data suggests a recovery and even an increase in line with the growth of disposable income. The proportion of taxed products in total fast-moving consumer goods purchases increased from 5.9 % (95 % CI: 5.7 % to 6.0 %) in 2010 to 7.4 % (95 % CI: 7.3 % to 7.6 %) in 2018. Furthermore, the tax has contributed to increased inequality as low-income households spend a higher proportion of their total expenditure on it. Although taxes on unhealthy foods have proven effective in the short-term, they may not be adequate for reducing overall consumption in the long-term, particularly as disposable income increases. In conclusion, implementing complex interventions is necessary to achieve sustainable positive changes in dietary habits.

A multimodal deep learning architecture for smoking detection with a small data approach.

Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data.

Impact of regulatory tightening of the Hungarian tobacco retail market on availability, access and cigarette smoking prevalence of adolescents.

INTRODUCTION: Policies that reduce tobacco retail density to decrease tobacco use among the youth are critical for the tobacco endgame. This paper reviews a Hungarian tobacco regulatory measure, which, since 2013, has confined the sale of tobacco products exclusively to so-called National Tobacco Shops, summarises the changes in the national tobacco retail marketplace and reports on analyses of the impact of this intervention on illegal sales to minors and adolescent smoking behaviour. METHODS: We reviewed the available national statistical data on the structure and dynamics of the tobacco retail market. Changes in lifetime and current (past 30 days) use of cigarettes among Hungarian adolescents aged 13-17 years were assessed using data from international youth surveys on health behaviours collected in 2010-2020. RESULTS: Since the start of policy implementation, the density of tobacco shops in Hungary decreased by 85%, from 4.1 to 0.6 per 1000 persons. The prevalence of lifetime and current cigarette smoking among adolescents declined by 13-24 percentage points (pp) and by 4.8-15 pp, respectively. The rate of illegal sales of tobacco products to minors decreased by 27.6 pp, although the prevalence of compensatory access strategies, especially asking others to buy cigarettes for minors, increased. CONCLUSIONS: After a significant decrease in the nationwide availability of licensed tobacco retailers, Hungary experienced short-term reductions in youth smoking prevalence. However, the sporadic implementation of complementary, evidence-based tobacco control strategies might limit further declines in youth smoking initiation and tobacco product use.

Physical imaging parameter variation drives domain shift.

Statistical learning algorithms strongly rely on an oversimplified assumption for optimal performance, that is, source (training) and target (testing) data are independent and identically distributed. Variation in human tissue, physician labeling and physical imaging parameters (PIPs) in the generative process, yield medical image datasets with statistics that render this central assumption false. When deploying models, new examples are often out of distribution with respect to training data, thus, training robust dependable and predictive models is still a challenge in medical imaging with significant accuracy drops common for deployed models. This statistical variation between training and testing data is referred to as domain shift (DS).To the best of our knowledge we provide the first empirical evidence that variation in PIPs between test and train medical image datasets is a significant driver of DS and model generalization error is correlated with this variance. We show significant covariate shift occurs due to a selection bias in sampling from a small area of PIP space for both inter and intra-hospital regimes. In order to show this, we control for population shift, prevalence shift, data selection biases and annotation biases to investigate the sole effect of the physical generation process on model generalization for a proxy task of age group estimation on a combined 44 k image mammogram dataset collected from five hospitals.We hypothesize that training data should be sampled evenly from PIP space to produce the most robust models and hope this study provides motivation to retain medical image generation metadata that is almost always discarded or redacted in open source datasets. This metadata measured with standard international units can provide a universal regularizing anchor between distributions generated across the world for all current and future imaging modalities.

Perceived harm of heated tobacco products, e-cigarettes, and nicotine replacement therapy compared with conventional cigarettes among ever and current heated tobacco users.

Aims: There is limited knowledge on how ever and current heated tobacco product (HTP) users perceive the relative harm of various nicotine-containing products. The aim of this study was to explore relative harm perceptions of HTPs, e-cigarettes, and nicotine replacement therapy (NRT) relative to conventional cigarettes (CCs) among ever versus current HTP users, and exclusive (who use only a HTP) versus dual/poly-users (concurrent users of HTP and CCs and/or e-cigarettes). Methods: Data came from 1423 ever or current Hungarian adult HTP users who participated in a cross-sectional web-based survey in 2020. Unadjusted and adjusted logistic regression analyses were performed to explore the associations between relative harm perceptions, HTP use patterns, past tobacco use, HTP dependence, and socio-demographic variables. Results: 81.2% of the sample was current HTP users, of them 78.4% were exclusive HTP users. Compared to CCs, 86.2% of the sample perceived HTP to be less harmful, with current and exclusive HTP users endorsing more this belief, followed by NRT (79.8%), and e-cigarettes (45.2%). In general, neither socio-demographic variables nor past tobacco use, HTP use pattern, and HTP dependence were related to perceived harmfulness across the tested products. However, there was a specific pattern for each tested product with a set of explanatory variables. Conclusions: Ever/current HTP users presented misperceptions about the harms of HTPs, e-cigarettes, and NRT. They underestimated the potential health benefits of NRT and had distorted harm perceptions about HTPs and e-cigarettes. Public education about the relative harms of different nicotine and tobacco products is urgently needed.

The burden of mental disorders, substance use disorders and self-harm among young people in Europe, 1990-2019: Findings from the Global Burden of Disease Study 2019.

Background: Mental health is a public health issue for European young people, with great heterogeneity in resource allocation. Representative population-based studies are needed. The Global Burden of Disease (GBD) Study 2019 provides internationally comparable information on trends in the health status of populations and changes in the leading causes of disease burden over time. Methods: Prevalence, incidence, Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) from mental disorders (MDs), substance use disorders (SUDs) and self-harm were estimated for young people aged 10-24 years in 31 European countries. Rates per 100,000 population, percentage changes in 1990-2019, 95% Uncertainty Intervals (UIs), and correlations with Sociodemographic Index (SDI), were estimated. Findings: In 2019, rates per 100,000 population were 16,983 (95% UI 12,823 - 21,630) for MDs, 3,891 (3,020 - 4,905) for SUDs, and 89·1 (63·8 - 123·1) for self-harm. In terms of disability, anxiety contributed to 647·3 (432-912·3) YLDs, while in terms of premature death, self-harm contributed to 319·6 (248·9-412·8) YLLs, per 100,000 population. Over the 30 years studied, YLDs increased in eating disorders (14·9%;9·4-20·1) and drug use disorders (16·9%;8·9-26·3), and decreased in idiopathic developmental intellectual disability (-29·1%;23·8-38·5). YLLs decreased in self-harm (-27·9%;38·3-18·7). Variations were found by sex, age-group and country. The burden of SUDs and self-harm was higher in countries with lower SDI, MDs were associated with SUDs. Interpretation: Mental health conditions represent an important burden among young people living in Europe. National policies should strengthen mental health, with a specific focus on young people. Funding: The Bill and Melinda Gates Foundation.

Lifelong vaccination strategy as a tool against pandemics in Hungary / Élethosszan át tartó oltási stratégia mint eszköz a pandémiák elleni védekezésben Magyarországon

Due to various factors, the chances of infectious disease emergence or re-emergence have increased in the 21st century, thus, the likelihood of new emerging pandemics has also increased. The COVID-19 pandemic, which appeared in 2019, has highlighted that certain new and re-emerging infectious diseases - in the case of lack or delay in effective measures - can spread very rapidly. The main tool for the fight against infectious diseases is immunization through vaccination. While focusing on the personal health, public health, economic and societal benefits of a lifelong immunization strategy, especially in light of the aging society, the goal of this paper is to present the benefits of vaccines. In order to increase the added value of vaccinations it is recommended to create a lifelong immunization strategy.

Comparison of hesitancy between COVID-19 and seasonal influenza vaccinations within the general Hungarian population: a cross-sectional study.

BACKGROUND: The willingness to get COVID-19 or seasonal influenza vaccines has not yet been thoroughly investigated together, thus, this study aims to explore this notion within the general adult population. METHODS: The responses of 840 Hungarian participants were analysed who took part in a nationwide computer-assisted telephone interviewing. During the survey questions concerning various demographic characteristics, perceived financial status, and willingness to get the two types of vaccines were asked. Descriptive statistics, comparative statistics and word co-occurrence network analysis were conducted. RESULTS: 48.2% of participants were willing to get a COVID-19 vaccine, while this ratio for the seasonal influenza was only 25.7%. The difference was significant. Regardless of how the participants were grouped, based on demographic data or perceived financial status, the significant difference always persisted. Being older than 59 years significantly increased the willingness to get both vaccines when compared to the middle-aged groups, but not when compared to the younger ones. Having higher education significantly elevated the acceptance of COVID-19 vaccination in comparison to secondary education. The willingness of getting any type of COVID-19 vaccine correlated with the willingness to get both influenza and COVID-19. Finally, those who were willing to get either vaccine coupled similar words together to describe their thoughts about a COVID-19 vaccination. CONCLUSION: The overall results show a clear preference for a COVID-19 vaccine and there are several similarities between the nature of willingness to get either type of vaccine.

Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis.

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.

"Your money or your life!" ­ Ransomwares in healthcare information systems / "Pénzt vagy életet!" - Zsarolóvírusok az egészségügyi informatikai rendszerekben.

In recent years, due to the value of health data and the specificities of health processes, data breaches have become increasingly important. In addition to the general data protection rules of the European Union, aspects of general information security, including technology and human behaviour, have been reassessed. In this article, we present the importance of blackmail (ransomware) virus attacks in the health sector. According to international data, especially in the US, one of the most important methods of institutional attacks will be the extortion attack in the coming years, and this is expected to increase in importance, especially in health care where sensitive and valuable data are truly life-giving. Because of the encryption of data and the blocking of core processes, blackmail viruses can also have a significant impact on the effectiveness of therapy and healthcare. In addition to presenting the current international situation, the article also outlines the most important steps that can be taken by those involved in daily patient's care to ensure continuity of patient care. Orv Hetil. 2020; 161(36): 1498-1505.