74 research outputs found

    Associations of e-cigarette experimentation with support for tobacco control policies in the European Union, 2012-2014

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    Introduction: There are limited data on the potential effects of e-cigarette experimentation on support for tobacco control policies. To bridge this gap, we assessed associations between e-cigarette experimentation and support for tobacco control policies in the European Union 2012-2014. We also investigated variations across tobacco-use status, e-cigarette experimentation and sociodemographic characteristics. Methods: Datasets were used from the Special Eurobarometer for Tobacco surveys performed in 2012 (n=26 751) and 2014 (n=27 801). Tobacco control policies assessed were: banning advertising, policies to keep tobacco out of sight, banning online sales, banning flavors, standardized packaging, tax increases, and policies to reduce illicit trade in tobacco. We use multilevel logistic regression models to assess variations in socio-demographics and tobacco/e-cigarette use with support for these policies in 2014, and examined changes in support for these policies, between 2012 and 2014, separately by tobacco-use status (never, current, and former smokers). Results: Population support for tobacco control policies was high in 2014: policies to reduce illicit trade had the highest level of support at 70.1%, while tax increases were the least likely measure to be supported with 52.3% support. Among never and former smokers, experimentation with e-cigarettes was associated with reduced support for all tobacco control policies assessed. For example, never smokers who had experimented with e-cigarettes were less likely to support either tobacco advertising bans (adjusted odds ratio aOR=0.57, 95% confidence interval 0.46-0.71) or standardized packaging for tobacco (aOR=0.58, 95% CI: 0.47-0.71). Former smokers who had experimented with e-cigarettes were less likely to either support standardized packaging for tobacco (aOR=0.70, 95% CI: 0.60-0.82) or keeping tobacco out of sight (aOR=0.77, 95% CI: 0.65-0.90). Among current smokers, e-cigarette experimentation was not associated with support for the tobacco control policies assessed. Conclusions: E-cigarette experimentation was consistently associated with reduced support for tobacco control policies among never and former smokers but not among current smokers. The implications of these findings for tobacco control are unknown, but the data support concerns that e-cigarette experimentation may affect public support for established tobacco control policies within specific subgroups. Further research is needed to assess potential long-term impacts on tobacco control policies

    Knowledge of the health risks of smoking and impact of cigarette warning labels among tobacco users in six European countries: Findings from the EUREST-PLUS ITC Europe Surveys

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    INTRODUCTION The aim of this study was to examine knowledge of health effects of smoking and the impact of cigarette package warnings among tobacco users from six European Union (EU) Member States (MS) immediately prior to the introduction of the EU Tobacco Products Directive (TPD) in 2016 and to explore the interrelationship between these two factors. METHODS Cross-sectional data were collected via face-to-face interviews with adult smokers (n=6011) from six EU MS (Germany, Greece, Hungary, Poland, Romania, Spain) between June-September 2016. Sociodemographic variables and knowledge of health risks of smoking (KHR) were assessed. Warning salience, thoughts of harm, thoughts of quitting and foregoing of cigarettes as a result of health warnings were assessed. The Label Impact Index (LII) was used as a composite measure of warning effects. Linear and logistic regression analyses were used to examine sociodemographic predictors of KHR and LII and the inter-relationship between knowledge and LII scores. RESULTS The KHR index was highest in Romania and Greece and lowest in Hungary and Germany. While the majority of smokers knew that smoking increases the risk for heart diseases, lung and throat cancer, there was lower awareness that tobacco use caused mouth cancer, pulmonary diseases, stroke, and there were very low levels of knowledge that it was also associated with impotence and blindness, in all six countries. Knowledge regarding the health risks of passive smoking was moderate in most countries. The LII was highest in Romania and Poland, followed by Spain and Greece, and lowest in Germany and Hungary. In almost all countries, there was a positive association between LII scores and higher KHR scores after controlling for sociodemographic variables. Several sociodemographic factors were associated with KHR and LII, with differences in these associations documented across countries. CONCLUSIONS These data provide evidence to support the need for stronger educational efforts and policies that can enhance the effectiveness of health warnings in communicating health risks and promoting quit attempts. Data will serve as a baseline for examining the impact of the TPD

    The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

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    BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

    Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019

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    Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2•72 (95% uncertainty interval [UI] 2•66–2•79) in 2000 to 2•31 (2•17–2•46) in 2019. Global annual livebirths increased from 134•5 million (131•5–137•8) in 2000 to a peak of 139•6 million (133•0–146•9) in 2016. Global livebirths then declined to 135•3 million (127•2–144•1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2•1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27•1% (95% UI 26•4–27•8) of global livebirths. Global life expectancy at birth increased from 67•2 years (95% UI 66•8–67•6) in 2000 to 73•5 years (72•8–74•3) in 2019. The total number of deaths increased from 50•7 million (49•5–51•9) in 2000 to 56•5 million (53•7–59•2) in 2019. Under-5 deaths declined from 9•6 million (9•1–10•3) in 2000 to 5•0 million (4•3–6•0) in 2019. Global population increased by 25•7%, from 6•2 billion (6•0–6•3) in 2000 to 7•7 billion (7•5–8•0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58•6 years (56•1–60•8) in 2000 to 63•5 years (60•8–66•1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation: Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

    Global burden of 87 risk factors in 204 countries and territories, 1990�2019: a systematic analysis for the Global Burden of Disease Study 2019

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    Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk�outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk�outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk�outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95 uncertainty interval UI 9·51�12·1) deaths (19·2% 16·9�21·3 of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12�9·31) deaths (15·4% 14·6�16·2 of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253�350) DALYs (11·6% 10·3�13·1 of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0�9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10�24 years, alcohol use for those aged 25�49 years, and high systolic blood pressure for those aged 50�74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

    Patterns, trends and determinants of e-cigarette use in 28 European Union Member States 2014–2017

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    There is a lack of nationally representative data on regular e-cigarette use, as well as on the transition from experimentation to regular use. This study examines changes in these in Europe between 2014 and 2017. Data come from the 2014 (n = 27,801) and 2017 (n = 27,901) adult Special Eurobarometer for Tobacco Survey, providing nationally representative surveys of 28 EU member states. We defined regular use of e-cigarettes as daily or weekly use from a question on frequency of e-cigarette use. Among ever users of e-cigarettes we assessed socio-demographic correlates of becoming a regular user. 1.5% of the EU population were regular e-cigarette users in 2014, which had risen to 1.8% in 2017. In 2017 63 million Europeans aged 15 or older had ever used e-cigarettes (95% CI, 59.9 million–66.2 million), and 7.6 million (95% CI, 6.5million–8.9 million) were regular e-cigarette users. Among those who had ever used e-cigarettes, participants aged 15–24 years were less likely to be regular user than those aged ≥55 years (16.9% vs. 38.1%), as were never smokers compared with current and former smokers (12.8% vs. 27.0% vs. 41.3%). The proportion of adults who were regular e-cigarette users in 2017 ranged from 4.7% in the UK to 0.2% in Bulgaria. There have been slight rises in the proportion of people regularly using e-cigarettes in the EU, and this varies considerably between member states, indicating the role of the regional environment in supporting or deterring e-cigarette use. © 2018 Elsevier Inc

    Trends and correlates of waterpipe use in the European Union: Analysis of selected eurobarometer surveys (2009-2017)

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    Introduction: The aim of this study was to assess the trends and correlates of waterpipe use between 2009 and 2017 in the 28 European Union (EU) member states. Methods: We analyzed data from wave 72.3 (2009, n = 27 788); wave 77.1 (2012, n = 26 751); wave 82.4 (2014, n = 27 801); and wave 87.1 (2017, n = 27 901) of the Eurobarometer survey. Representative samples of EU residents aged ≥15 years were asked to report ever use of waterpipe. Regular waterpipe use, i.e., at least once a month was also assessed in 2017. Associations of ever and current use with sociodemographic factors were assessed with multilevel logistic regression. Results: The prevalence of ever waterpipe use in the EU increased from 11.6% in 2009 to 16.3% in 2014 before dropping to 12.9% in 2017, but there was wide variation between EU member states, ranging from 2.3% (Croatia, 2009) to 41.7% (Latvia, 2017). Regular waterpipe use was highest in Austria (3.6%), Latvia (2.5%) and Belgium (2.0%) in 2017. Respondents aged 15-24 years were 11.43 times more likely (95% confidence interval [CI] = 10.71 to 12.21) to have ever used waterpipe compared to those 55 years and older. Regular and ever waterpipe use were also more likely among current and former cigarette smokers. Males (adjusted odds ratio [aOR] = 1.64; 95% CI = 1.58 to 1.70) and those living in urban areas (aOR = 1.36; 95% CI = 1.30 to 1.42) were more likely to have ever used waterpipe. Conclusion: A substantial proportion of EU citizens, especially young men, have tried waterpipe. Regular use is relatively limited, but more systematic surveillance is required to monitor trends across the EU. Implications: Data on waterpipe use in the European Union are scarce. The prevalence of ever waterpipe use in the EU increased from 11.6% in 2009 to 16.3% in 2014 before dropping to 12.9% in 2017, with wide variation between EU member states. Males, those living in urban areas, younger respondents, and current or former cigarette smokers were more likely to be ever or regular users of waterpipe. © The Author(s) 2017
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