Loss Of Life Expectancy Research Articles

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data

To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. Health Data Research UK.

Number of life-years lost at the time of diagnosis and several years post-diagnosis in patients with solid malignancies: a population-based study in the Netherlands, 1989-2019.

Loss of life expectancy (LOLE) may provide more intuitive information on the impact of cancer than relative survival over a fixed time horizon (e.g., 5-year relative survival). We aimed to assess the evolution of the LOLE using a nationwide, population-based cohort including patients diagnosed with one of 17 most frequent solid malignancies. From the Netherlands Cancer Registry, we selected adult patients diagnosed with one of the 17 most frequent solid malignancies in the Netherlands during 1989-2019, with survival follow-up until 2022. We used flexible parametric survival models to estimate the LOLE at diagnosis and the LOLE after surviving several years post-diagnosis (conditional LOLE; CLOLE) by cancer type, calendar year, age, sex, and disease stage. For all cancers combined, the LOLE consistently decreased from 1989 to 2019. This decrease was most pronounced for males with prostate cancer (e.g., from 6.9 [95% confidence interval [CI], 6.7-7.1] to 2.7 [95% CI, 2.5-3.0] for 65-year-olds) and females with breast cancer (e.g., from 6.6 [95% CI, 6.4-6.7] to 1.9 [95% CI, 1.8-2.0] for 65-year-olds). The LOLE among patients with cancers of the head and neck or the central nervous system remained constant over time. Overall, the CLOLE showed that the life years lost among patients with cancer decreased with each additional year survived post-diagnosis. For example, the LOLE at diagnosis for 65-year-old females diagnosed with breast cancer in 2019 was 1.9 [95% CI, 1.8-2.0] compared with 1.7 [95% CI, 1.6-1.8], 1.0 [95% CI, 0.9-1.1], and 0.5 [95% CI, 0.5-0.6] when surviving one, five, and ten years post-diagnosis, respectively. Estimates for other combinations of patient and tumour characteristics are available in a publicly available web-based application. Our findings suggested that the evolution of LOLE substantially varies across cancer type, age, and disease stage. LOLE estimates help patients better understand the impact of their specific cancer diagnosis on their life expectancy. None.

The effect of the COVID-19 pandemic on life expectancy in 27 countries

The expected year-on-year intrinsic mortality variations/changes are largely overlooked in the existing research when estimating the effect of the COVID-19 pandemic on mortality patterns. To fill this gap, this study provides a new assessment of the loss of life expectancy caused by COVID-19 in 27 countries considering both the actual and the expected changes in life expectancy between 2019 and 2020. Life expectancy in 2020 and the expected life expectancy in the absence of COVID-19 are estimated using the Lee-Carter model and data primarily from the Human Mortality Database. The results show that life expectancy in 21 of the 27 countries was expected to increase in 2020 had COVID-19 not occurred. By considering the expected mortality changes between 2019 and 2020, the study shows that, on average, the loss of life expectancy among the 27 countries in 2020 amounted to 1.33 year (95% CI 1.29–1.37) at age 15 and 0.91 years (95% CI 0.88–0.94) at age 65. Our results suggest that if the year-on-year intrinsic variations/changes in mortality were considered, the effects of COVID-19 on mortality are more profound than previously understood. This is particularly prominent for countries experiencing greater life expectancy increase in recent years.

Progress in research of relationship between metal or metalloid and persistent organic pollutants exposures and cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of mortality and healthy life expectancy loss, ranking first in causing the global burden of disease. In addition to the traditional CVD risk factors, such as hypertension and diabetes, environmental chemical pollutants may also play a role in the development of CVD. This paper summarizes the evidence regarding the relation of exposures to metal or metalloid and persistent organic pollutants with risk for CVD and introduces the research progress in the relation between the exposures to two environmental chemical pollutants and CVD risk. The study aims to provide scientific evidence for the effective prevention of CVD through the management of chemical pollutants in environment.

Mapping Life Expectancy Loss in Barcelona in 2020

We use a Bayesian spatio-temporal model, first to smooth small-area initial life expectancy estimates in Barcelona for 2020, and second to predict what small-area life expectancy would have been in 2020 in absence of covid-19 using mortality data from 2007 to 2019. This allows us to estimate and map the small-area life expectancy loss, which can be used to assess how the impact of covid-19 varies spatially, and to explore whether that loss relates to underlying factors, such as population density, educational level, or proportion of older individuals living alone. We find that the small-area life expectancy loss for men and for women have similar distributions, and are spatially uncorrelated but positively correlated with population density and among themselves. On average, we estimate that the life expectancy loss in Barcelona in 2020 was of 2.01 years for men, falling back to 2011 levels, and of 2.11 years for women, falling back to 2006 levels.

Impact of the COVID-19 pandemic on the age-sex pattern of COVID-19 deaths in India

ABSTRACT The SARS-CoV-2 virus (termed COVID-19) raised the mortality trends and diminished life expectancy globally. This study analyzes the age-sex patterns of COVID-19 deaths in India during 2020–2021 and how the age-sex patterns have changed between 2020 and 2021. We calculated the losses in the male-female life expectancy at birth (LEB) induced due to the pandemic by projecting pre-pandemic mortality trends into 2020 and 2021. The finding suggested that mortality rates have increased substantially due to COVID-19 in India, implying almost four years of losses of LEB in 2021. The age pattern of mortality differs by the years of the pandemic. In 2020, the disease disproportionately killed older people. In 2021, with the outbreak of COVID-19 delta variant infection, mortality also rose among younger adults. Moreover, in 2020, COVID-19 deaths were higher among males, whereas in 2021, mortality increased for both sexes. In conclusion, COVID-19 triggered a significant increase in mortality in India between 2020 and 2021, dragging the country to a lower LEB level in 2010–2011. The future impact of the pandemic is yet to be seen. Also, distinct age and sex differential COVID-19 health and socioeconomic impacts compel future researchers to include demographic heterogeneity in their study.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020.

Nitrogen dioxide (NO2) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO2 air quality guideline (AQG) (annual average <10 μg/m3). However, the burden of diseases attributable to long-term NO2 exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO2; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO2 exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO2 concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO2 exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO2-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO2 exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO2-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO2 exposure but also minimizing inequality.

Differential impact of the COVID-19 pandemic on excess mortality and life expectancy loss within the Hispanic population.

The impact of the COVID-19 pandemic on the Hispanic population resulted in the almost complete elimination of the long-standing Hispanic mortality advantage relative to the non-Hispanic White population. However, it is unknown how COVID-19 mortality affected the diverse Hispanic subpopulations. We estimate life expectancy at birth in 2019 and 2020 by select Hispanic country/region of origin and explore how changes in age-specific all-cause and COVID-19 mortality affected changes in life expectancy between 2019 and 2020 for each group. We use final 2019 and 2020 mortality data from the National Center for Health Statistics and population estimates based on the 2019 and 2020 American Community Survey. We calculate life tables and apply decomposition techniques to explore the effects of changes in age- and cause-specific mortality on life expectancy. Patterns of age- and cause-specific excess deaths and their impact on declines in life expectancy due to the COVID-19 pandemic differed substantially by Hispanic subgroup. Life expectancy losses ranged from 0.6 to 6.7 years among males and from 0.6 to 3.6 years among females. Our findings highlight the heterogeneous impact of the COVID-19 pandemic within the Hispanic population. Our findings contribute new information that will help future researchers identify the causes of the disproportionately severe impact of the COVID-19 pandemic on the Hispanic population. Our study underscores the importance of population disaggregation in endeavors to identify the multiple pathways by which the pandemic affected the Hispanic population.

Loss of life expectancy due to stroke and its subtypes in urban and rural areas in China, 2005–2020

Stroke is characterised by high mortality and disability rate in China. This study aimed to explore the temporal trends in years of life lost (YLL) and loss of life expectancy...

Influence of poor sleep on cardiovascular disease-free life expectancy: a multi-resource-based population cohort study

BackgroundThe complexity of sleep hinders the formulation of sleep guidelines. Recent studies suggest that different unhealthy sleep characteristics jointly increase the risks for cardiovascular disease (CVD). This study aimed to estimate the differences in CVD-free life expectancy between people with different sleep profiles.MethodsWe included 308,683 middle-aged adults from the UK Biobank among whom 140,181 had primary care data linkage. We used an established composite sleep score comprising self-reported chronotype, duration, insomnia complaints, snoring, and daytime sleepiness to derive three sleep categories: poor, intermediate, and healthy. We also identified three clinical sleep disorders captured by primary care and inpatient records within 2 years before enrollment in the cohort: insomnia, sleep-related breathing disorders, and other sleep disorders. We estimated sex-specific CVD-free life expectancy with three-state Markov models conditioning on survival at age 40 across different sleep profiles and clinical disorders.ResultsWe observed a gradual loss in CVD-free life expectancy toward poor sleep such as, compared with healthy sleepers, poor sleepers lost 1.80 [95% CI 0.96–2.75] and 2.31 [1.46–3.29] CVD-free years in females and males, respectively, while intermediate sleepers lost 0.48 [0.41–0.55] and 0.55 [0.49–0.61] years. Among men, those with clinical insomnia or sleep-related breathing disorders lost CVD-free life by 3.84 [0.61–8.59] or 6.73 [5.31–8.48] years, respectively. Among women, sleep-related breathing disorders or other sleep disorders were associated with 7.32 [5.33–10.34] or 1.43 [0.20–3.29] years lost, respectively.ConclusionsBoth self-reported and doctor-diagnosed poor sleep are negatively associated with CVD-free life, especially pronounced in participants with sleep-related breathing disorders.

Assessing the impact of one million COVID-19 deaths in America: economic and life expectancy losses

Between February 2020 and May 2022, one million Americans have died of COVID-19. To determine the contribution of those deaths to all-cause mortality in terms of life expectancy reductions and the resulting economic welfare losses, we calculated their combined impact on national income growth and the added value of lives lost. We estimated that US life expectancy at birth dropped by 3.08 years due to the million COVID-19 deaths. Economic welfare losses estimated in terms of national income growth supplemented by the value of lives lost, was in the order of US$3.57 trillion. US$2.20 trillion of these losses were in in the non-Hispanic White population (56.50%), US$698.24 billion (19.54%) in the Hispanic population, and US$579.93 billion (16.23%) in the non-Hispanic Black population. The scale of life expectancy and welfare losses underscores the pressing need to invest in health in the US to prevent further economic shocks from future pandemic threats.

Ability of STAR-CAP staging system to prognosticate risk of subsequent therapies after initial treatment of M0 prostate cancer.

310 Background: Given lower mortality rates and life expectancy loss in prostate cancer, treatment regimens must consider long-term outcomes with quality of life. Aggressiveness of treatment impacts severity of side effects and depends on the highly heterogeneous staging of the disease at onset. Staging and risk-classification systems have been developed, including STAR-CAP staging system which informs prognosis and could help with treatment intensification decisions. Compared with existing models such as the AJCC, STAR-CAP had superior performance and prognostic power for disease mortality prediction. However the risk of requiring subsequent therapies (ie salvage radiation therapy or androgen deprivation therapies after initial treatment) has not been described in STAR-CAP cohorts. Quantifying risk of subsequent therapies after initial therapies is clinically useful to help patients decide between various management strategies and whether monotherapies or multi-modality therapies may be sufficient. Methods: Patients who could be stratified by STAR-CAP system and underwent surgery or radiation therapy were identified from an institutional observational registry. Patients treated before 2010 were gathered retrospectively (n=835) and prospectively thereafter (n=2588). The primary endpoint was progression to additional therapy after primary treatment (radiation, surgery, or systemic therapy). Risk of progression accounting for STAR-CAP group was estimated using the Fine and Gray Competing risk model, with death as the competing risk. P&lt;0.05 was considered statistically significant. Results: 3424 men diagnosed with prostate cancer underwent curative-intent treatment with surgery or radiation therapy (RT) between 1995 – 2022. The median age at diagnosis was 64.9 years (IQR 59.4 to 70.0); median follow-up time was 5.4 years. Patients with STAR-CAP 1A-1C had a statistically similar risk of requiring additional therapies, whereas those with 2A to 3B disease had a significantly and incrementally higher risk of requiring additional therapies (by group, hazard ratios and 95% CI: 2A, 1.8 (1.2 to 2.5); 2B, 2.9 (2.1 to 4.2); 2C, 3.4 (2.4 to 4.9); 3A, 5.7 (3.9 to 8.3); 3B, 7.0 (4.4 to 11.1), all p&lt;0.001). The 5-year risks of requiring additional therapy after surgery for a Stage 1A, 2A, and 3A patient is 9.7%, 16.2%, and 38%, respectively. The 5-year risks of requiring additional therapy after radiation with or without ADT for a Stage 1A, 2A, and 3A patient are 7.3%, 10.4% and 27.9%, respectively. Conclusions: The STAR-CAP grouping system can prognosticate risk of requiring additional therapies after primary treatment of localized prostate cancer. The lower risk of subsequent therapy seen in RT patients is likely due to use of ADT with RT. Using STAR-CAP to prognosticate risk of subsequent therapies after primary treatment can help guide treatment intensification decisions upfront.

Losses of Life Expectancy and Productivity Associated with COVID-19 Pandemic in Canada: Policy Implication for Future Communicable Disease Control.

This research examines whether the Coronavirus disease 2019 (COVID-19) did harm to the population's health through comparing the changes in the life expectancy of Canadians with those of Australians over the period from March 2019 to February 2021 by using a difference-in-differences (DID) estimation method. We found that the pandemic did cause differences in life expectancies between Canada and Australia, probably because of different initial control policies for COVID-19. This study uses the indicator of disability-adjusted life years (DALYs) to measure the societal health burden, which was corroborated by estimating temporal productivity loss (TPL) and permanent productivity loss (PPL) based on the human capital approach (HCA) using data from Health Canada. The societal health burden in Canada amounted to 6.493 DALYs per 1000 male persons and 5.316 DALYs per 1000 female persons. The economy's permanent productivity loss was around USD 5.3 billion, while the temporary productivity loss was around USD 3 billion from February 2020 to April 2022. The sum of the above two losses amounted to 0.477% of the GDP in 2019. Swift and decisive decisions at the very early stage of a pandemic can nip contagions in the bud before numbers get out of hand and would be less damaging to people's health and the economy, as seen in Australia, in contrast to what happened in Canada. We thus recommend that such policies plus telecommunication systems in healthcare services be implemented early on to cope with the future outbreak of any emerging infectious diseases such as COVID-19.

Вплив COVID-19 на смертність і тривалість життя в Україні в 2020—2021 рр.

With the increasing intensity of movement of people between countries and continents, humanity has become more vulnerable to the spread of diseases on a global scale. The rapid spread of COVID-19 in 2020 has led to a significant shift in the mortality structure of the population and tangible losses in average life expectancy. Governments of different countries have responded differently to this challenge. Therefore, it is relevant to compare the experience of Ukraine against the other countries. The purpose of this article is to analyze and quantify the impact of COVID-19 on mortality and life expectancy in Ukraine. The novelty is an estimate of the impact of COVID-19 on mortality in Ukraine by different methods based on the data for the complete years 2020-2021. Methods of calculation and analysis of demographic indicators, life tables, graphic method, decomposition method, and Lee—Carter method were used. The existing definitions of excess mortality are analyzed. Based on the use of 6 methods it is determined that the pandemic led to an increase in the number of deaths in Ukraine (without Donbas and Crimea) from 92.7 to 241.5 thousand. Most estimates fall into the range of 147.5-224.2 thousand. It is shown that the biggest number of excess deaths is observed in older age groups, especially 65-84 years. The highest loss of life potential occurred in the 65-74 age group. In 2020-2021, life expectancy at birth for women decreased by 2.62 years, while for men the reduction was 1.77 years. The increase in mortality from COVID-19 resulted in a loss of 1.91 and 1.51 years, respectively. In 2020-2021, a sharp increase in mortality from respiratory diseases was recorded. It is shown that mortality from this class of diseases has a direct strong (correlation coefficient 0.91) and significant (p&lt; 0.001) correlation with mortality from COVID-19. The relationship between these causes of death is supported by a similar effect on the age pattern of life expectancy losses. The increase in the overall male mortality rate was significantly mitigated by decrease in mortality from infectious and parasitic diseases, as well as external causes of death. Women, due to their much lower mortality rates from these classes of causes, have a markedly lower potential for improvement in this area. Therefore, it is necessary to expand and facilitate access to professional medical care and not limit it, as was done during lockdowns and quarantines.

Assessment of shifts in mortality age profile of adult population in Moscow during pandemic

Introduction. The COVID-19 pandemic induced the global crisis of mortality. Delayed medical care and reduced availability under quarantine restrictions have led to an increase in mortality not only from Covid-19, but also from chronic non-communicable diseases, affecting men and women of different ages to varying degrees. The study purpose is to assess shifts in mortality age profile of population aged 20+ in Moscow during COVID-19 pandemic. Materials and methods. Official vital statistics of Rosstat, operational information from civil registration database on death causes in Moscow. Results. The gain in mortality rate caused primarily by deaths from COVID-19 interrupted in 2020 the positive demographic dynamics that had developed over the previous years. In 2021, the increase in the mortality rate continued, but the loss of life expectancy was lower due to a decrease in mortality in older ages and the return of most indicators to the level of 2019. However, from a socio-economic point of view, the situation in 2021 developed more negatively, since the age structure of life expectancy losses is younger than in 2020. Limitations. The study results are based on operational information of the Moscow civil registration office and could be used for the development of managerial decisions for Moscow only. Conclusion. Against the pandemic demographic situation in Moscow has worsened. Due to mortality gain in the Russia’s capital first in the past years recorded natural population decline.

Analysis of the disease burden trend of malignant tumors of the female reproductive system in China from 2006 to 2020

BackgroundMalignant tumors of reproductive system seriously threaten women’s life and health. We analyzed the changes in mortality and disease burden of cervical cancer, uterine cancer and ovarian cancer in China from 2006 to 2020 to provide a basis for formulating scientific prevention and control measures.MethodsAnnual death data for cervical cancer, uterine cancer and ovarian cancer were collected from the Chinese Cause of Death Surveillance. The crude mortality rate (CMR), age-standardized mortality rate (ASMR), annual percentage change (APC), and average APC (AAPC) were applied to analyze the trend of mortality. Loss of life expectancy (LLE) and years of life lost (YLL) were used to assess disease burden.ResultsFrom 2006 to 2020, there was no significant change in the total ASMR and standardized YLL rates of malignant tumors of the reproductive system, leading to an average LLE of 0.18 years. The YLL rate was the highest in the 55–59 age group. The mortality rate and disease burden of the three types of cancer have changed from uterine cancer higher than cervical cancer and ovarian cancer in 2006 to cervical cancer higher than ovarian cancer and uterine cancer in 2020. The ASMR and standardized YLL rate of uterine cancer showed a downward trend, and AAPC was − 5.21% (− 9.31% ~ − 0.91%) and − 6.07% (− 9.45% ~ − 2.58%), respectively. The mortality rates of cervical cancer and ovarian cancer remain high.ConclusionThe mortality and disease burden of malignant tumors of the female reproductive system in China are still at a high level. It is necessary to improve screening and prevention strategies as soon as possible, improve the techniques of diagnosis and treatment, and take adequate measures to protect women's life and health.

Life expectancy and Covid: Setting the record straight

Life expectancy (LE) is an objective and highly reliable marker for events affecting demography. Analysing LE changes during the Covid pandemic shows widely different situations in a sample of 29 countries, highlighting comparatively efficient management in most Western European countries, in contrast to catastrophic results in Eastern Europe and in the United States. Loss of LE is also inversely correlated with vaccination uptake, confirming the efficacy of vaccines at the population level.

RWD41 Colorectal Cancer Screening Program Saved More Life Expectancy in Females and More Employment Duration in Males: Empirical Evidence From Analyzing Nationwide Data Followed for 15 Years in Taiwan

We estimated the potential saving of loss-of-life expectancy (LE) and loss-of-lifetime employment duration (LED) to evaluate the effectiveness of National Colorectal Cancer Screening Program (NCCSP).

Mortality benefits of reduction fine particulate matter in Vietnam, 2019.

Studies assessing the health benefits of air pollution reduction in Vietnam are scarce. This study quantified the annual mortality burden due to PM2.5 pollution in Vietnam above the World Health Organization recommendation for community health (AQG: 5 μg/m3) and the proposed National Technical Regulation on Ambient Air Quality (proposed QCVN: 15 μg/m3). This study applied a health impact assessment methodology with the hazard risk function for non-communicable diseases (NCDs) and lower respiratory infections (LRIs) in the Global Exposure Mortality Model (GEMM) to calculate attributable deaths, Years of Life lost, and Loss of Life expectancy at birth due to air pollution in the Vietnamese population above 25 years of age in 11 provinces. We obtained annual average PM2.5 concentrations for Vietnam in 2019 at a 3x3 km grid modeled using Mixed Linear regression and multi-data sources. Population and baseline mortality data were obtained from administrative data system in Vietnam. We reported the findings at both the provincial and smaller district levels. Annual PM2.5 concentrations in all studied provinces exceeded both the AQG and the proposed QCVN. The maximum annual number of attributable deaths in the studied provinces if they had complied with WHO air quality guidelines was in Ha Noi City, with 5,090 (95%CI: 4,253-5,888) attributable deaths. At the district level, the highest annual rate of attributable deaths if the WHO recommendation for community health had been met was 104.6 (95%CI: 87.0-121.5) attributable deaths per 100,000 population in Ly Nhan (Ha Nam province). A much larger number of premature deaths in Vietnam could potentially be avoided by lowering the recommended air quality standard. These results highlight the need for effective clean air action plans by local authorities to reduce air pollution and improve community health.

On the impacts of the COVID-19 pandemic on mortality: Lost years or lost days?

To quantify the (direct and indirect) impacts of the COVID-19 pandemic on mortality for actual populations of persons living in 12 European countries in 2020. Based on demographic and mortality data, as well as remaining life expectancies found in the Human Mortality Database, we calculated a "population life loss" in 2020 for men and women living in Belgium, Croatia, Denmark, Finland, Hungary, Lithuania, Luxembourg, Norway, Portugal, Spain, Sweden, and Switzerland. This quantity was obtained by dividing the total number of years lost in 2020 (estimated from all-cause mortality data and attributed directly or indirectly to COVID-19) by the size of the population. A significant population life loss was found in 8 countries in 2020, with men losing an average of 8.7, 5.0, 4.4, 4.0, 3.7, 3.4, 3.1, and 2.7 days in Lithuania, Spain, Belgium, Hungary, Croatia, Portugal, Switzerland, and Sweden, respectively. For women, this loss was 5.5, 4.3, 3.7, 3.7, 3.1, 2.4, 1.6, and 1.4 days, respectively. No significant losses were found in Finland, Luxembourg, Denmark and Norway. Life loss was highly dependent on age, reaching 40 days at the age of 90 in some countries, while only a few significant losses occurred under the age of 60. Even in countries with a significant population life loss in 2020, it was on average about 30 times lower than in 1918, at the time of the Spanish flu. Our results based on the concept of population life loss were consistent with those based on the classical concept of life expectancy, confirming the significant impact of COVID-19 on mortality in 8 European countries in 2020. However, while life expectancy losses were typically counted in months or years, population life losses could be counted in days, a potentially useful piece of information from a public health perspective.