World Stroke Organization (WSO): Global Stroke Fact Sheet 2022

Stroke remains the second-leading cause of death and the third-leading cause of death and disability combined (as expressed by disability-adjusted life-years lost – DALYs) in the world. The estimated global cost of stroke is over US$721 billion (0.66% of the global GDP). From 1990 to 2019, the burden (in terms of the absolute number of cases) increased substantially (70.0% increase in incident strokes, 43.0% deaths from stroke, 102.0% prevalent strokes, and 143.0% DALYs), with the bulk of the global stroke burden (86.0% of deaths and 89.0% of DALYs) residing in lower-income and lower-middle-income countries (LMIC). This World Stroke Organisation (WSO) Global Stroke Fact Sheet 2022 provides the most updated information that can be used to inform communication with all internal and external stakeholders; all statistics have been reviewed and approved for use by the WSO Executive Committee as well as leaders from the Global Burden of Disease research group.

Life-years lost by COVID-19 patients in public hospitals of Marseille (APHM-South-Eastern France): a limited death toll: a retrospective analysis

ObjectiveBetween 1 March and 15 June, France experienced the first wave of the COVID-19 pandemic, during which 29 549 deaths occurred among COVID-19 patients, 17 250 of whom died in hospital. Our hypothesis is that crude mortality rates are not sufficient to assess the impact of the epidemic on public health. The objective of this paper is to estimate the potential years of life lost (YLL) of patients who died from COVID-19.MethodWe realised a retrospective analysis of the exhaustive sample of COVID-19 PCR-positive patients who died in public hospitals of Marseille during this first wave. Data on demographic characteristics, comorbidities and care pathways were collected from medical records. The Charlson Comorbidity Index (CCI) was used to assess what would have been the probability of dying within 1 year of these patients in the absence of COVID-19 and to estimate total YLL.ResultsAmong the 1631 patients who were hospitalised for COVID-19, 178 patients died, at an average age of 80 years. According to CCI, 88.8% of the deceased patients had an 85% probability of dying within 1 year before COVID-19. Among the 11.2% who had a lower CCI probability, 18 out of 20 had at least one additional comorbidity known to be a major risk factor of mortality in COVID-19 disease. Cumulative total number of YLL was estimated to be 541 in this deceased population, that is, an average of 3 YLL.ConclusionAlthough our results should be interpreted with caution, this analysis confirms that mortality due to COVID-19 translates into a limited number of YLL due to both old age and preexisting comorbidities in the most vulnerable patients. This fact should be better considered in public health management of the pandemic both for risk communication and design of the most appropriate protective measures.

Investigating the Health Effects of 3 Coexisting Tobacco-Related Products Using System Dynamics Population Modeling: An Italian Population Case Study

With the proliferation of tobacco products, there might be a need for more complex models than current two-product models. We have developed a three-product model able to represent interactions between three products in the marketplace. We also investigate if using several implementations of two-product models could provide sufficient information to assess 3 coexisting products. Italy is used as case-study with THPs and e-cigarettes as the products under investigation. We use transitions rates estimated for THPs in Japan and e-cigarettes in the USA to project what could happen if the Italian population were to behave as the Japanese for THP or USA for e-cigarettes. Results suggest that three-product models may be hindered by data availability while two product models could miss potential synergies between products. Both, THP and E-Cigarette scenarios, led to reduction in life-years lost although the Japanese THP scenario reductions were 3 times larger than the USA e-cigarette projections.

Estimating the reduction in US mortality if cigarettes were largely replaced by e-cigarettes

Background Recent estimates indicated substantially replacing cigarettes by e-cigarettes would, during 2016–2100, reduce US deaths and life-years lost (millions) by 6.6 and 86.7 (Optimistic Scenario) and 1.6 and 20.8 (Pessimistic). To provide additional insight we use alternative modelling based on a shorter period (1991–2040), four main smoking-associated diseases, deaths aged 30–79 years, and a full product history. We consider variations in: assumed effective dose of e-cigarettes versus cigarettes (F); their relative quitting rate (Q); proportions smoking after 10 years (X); and initiation rate (I) of vaping, relative to smoking. Methods We set F = 0.05, X = 5%, Q = 1.0 and I = 1.0 (Main Scenario) and F = 0.4, X = 10%, Q = 0.5 and I = 1.5 (Pessimistic Scenario). Sensitivity Analyses varied Main Scenario parameters singly; F from 0 to 0.4, X 0.01% to 15%, and Q and I 0.5 to 1.5. To allow comparison with prior work, individuals cannot be dual users, re-initiate, or switch except from cigarettes to e-cigarettes. Results Main Scenario reductions were 2.52 and 26.23 million deaths and life-years lost; Pessimistic Scenario reductions were 0.76 and 8.31 million. These were less than previously, due to the more limited age-range and follow-up, and restriction to four diseases. Reductions in deaths (millions) varied most for X, from 3.22 (X = 0.01%) to 1.31 (X = 15%), and F, 2.74 (F = 0) to 1.35 (F = 0.4). Varying Q or I had little effect. Conclusions Substantial reductions in deaths and life-years lost were observed even under pessimistic assumptions. Estimates varied most for X and F. These findings supplement literature indicating e-cigarettes can importantly impact health challenges from smoking.

Direct Healthy Life Expectancy Estimates from Life Tables with a Sullivan Extension: The case of Brazil 2003

The Healthy Life Expectancy (HLE) in Brazil 2003 was estimated by Romero et al (2005) by using the Sullivan method and data from the World Health Survey carried out in Brazil in 2003. Here we use a Direct method to estimate the Healthy Life Years Lost (HLYL) and then the HLE. This is done after the analytic derivation of a more general model of survival-mortality and the estimation of a parameter bx related to the HLYL is followed by the formulation of a computer program providing results similar to those of the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. This program is an extension of classical life table including more columns to estimate the cumulative mortality, the average mortality, the person life years lost, and finally the HLYL parameter bx. Even more, a further extension of the Excel program based on the Sullivan method provides estimates of the Healthy Life Expectancy at every year of the lifespan.

Excess mortality from COVID and non-COVID causes in minority populations

The 2020 US mortality totaled 2.8 million after early March, which is 17.3% higher than age-population–weighted mortality over the same time interval in 2017 to 2019, for a total excess death count of 413,592. We use data on weekly death counts by cause, as well as life tables, to quantify excess mortality and life years lost from both COVID-19 and non–COVID-19 causes by race/ethnicity, age, and gender/sex. Excess mortality from non–COVID-19 causes is substantial and much more heavily concentrated among males and minorities, especially Black, non-Hispanic males, than COVID-19 deaths. Thirty-four percent of the excess life years lost for males is from non–COVID-19 causes. While minorities represent 36% of COVID-19 deaths, they represent 70% of non–COVID-19 related excess deaths and 58% of non–COVID-19 excess life years lost. Black, non-Hispanic males represent only 6.9% of the population, but they are responsible for 8.9% of COVID-19 deaths and 28% of 2020 excess deaths from non–COVID-19 causes. For this group, nearly half of the excess life years lost in 2020 are due to non–COVID-19 causes.

Public health impact of a US ban on menthol in cigarettes and cigars: a simulation study

IntroductionThe US Food and Drug Administration most recently announced its intention to ban menthol cigarettes and cigars nationwide in April 2021. Implementation of the ban will require evidence that it would improve public health. This paper simulates the potential public health impact of a ban on menthol in cigarettes and cigars through its impacts on smoking initiation, smoking cessation and switching to nicotine vaping products (NVPs).MethodsAfter calibrating an established US simulation model to reflect recent use trends in cigarette and NVP use, we extended the model to incorporate menthol and non-menthol cigarette use under a status quo scenario. Applying estimates from a recent expert elicitation on the behavioural impacts of a menthol ban, we developed a menthol ban scenario with the ban starting in 2021. We estimated the public health impact as the difference between smoking and vaping-attributable deaths and life-years lost in the status quo scenario and the menthol ban scenario from 2021 to 2060.ResultsAs a result of the ban, overall smoking was estimated to decline by 15% as early as 2026 due to menthol smokers quitting both NVP and combustible use or switching to NVPs. These transitions are projected to reduce cumulative smoking and vaping-attributable deaths from 2021 to 2060 by 5% (650 000 in total) and reduce life-years lost by 8.8% (11.3 million). Sensitivity analyses showed appreciable public health benefits across different parameter specifications.Conclusions and relevanceOur findings strongly support the implementation of a ban on menthol in cigarettes and cigars.

741Life expectancy among persons with a disease: the Life Years Lost method and R-package ‘lillies’

Focus of Presentation Life Years Lost represent the reduction in life expectancy for a group of persons, e.g. those with a disease. Calculation of life expectancy among those with a disease is not straightforward for diseases that are not present at birth, and previous studies considered a fixed age-of-onset, e.g. 15 years. A recently-introduced method takes into account the real age-of-onset, and allows to decompose differences according to different causes of death. The aim of this communication is to introduce the Life Years Lost method and the associated R package ‘lillies’. Findings The method uses age at onset for each person with a disease as its starting point and estimates the expected residual lifetime at that age using age-specific mortality rates among the diseased. The number of excess Life Years Lost is estimated by comparing the expected residual lifetime with that of the reference population of same age. A single estimate of excess Life Years Lost is estimated as the average of all the person-specific Life Years Lost, and is interpreted as the average number of Life Years Lost that patients with a given disease experience in excess to those experienced by a reference population of same age. Conclusions/Implications The Life Years Lost method provides accurate estimates of reduced life expectancy in persons with a disease and allows to decompose the total reduction into specific causes of death. Key messages The implementation of the Life Years Lost method – which can be used with individual-level data (e.g. electronic healthcare records) or summary data – overcomes past limitations in the estimation of life expectancy for time-varying conditions.