BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

The cause-specific mortality rate and standardized mortality ratio in patients with atrial fibrillation according to ICD-10 code (the first code).

Mortality indicators standardized by sex and cause of death. Year: 2010. Territory: Province of Lucca. Indicators: number of deaths observed, number of deaths expected, number of excess deaths, standardised mortality ratio. Standardisation method applied: indirect. Reference population (standard population): Residents in the geographical area of the Centre (Tuscany, Umbria, Marche, Lazio). Primary data source (number of deaths observed, population resident in the province of Lucca, sex- and age-specific mortality rates of the population resident in the Centro district): ISTAT. Calculation of indicators: Statistical Office of the Province of Lucca.

The dataset was originally created to allow the construction of age-specific mortality series and cohort mortality series for particular diseases, from the mid-nineteenth century to the present (in conjunction with the comparable mortality database created by the Office of National Statistics which covers 1901 – present). The dataset is fairly comprehensive and therefore allows both fine analysis of trends in single causes and also the construction of consistent aggregated categories of causes over time. Additionally, comparison of trends in individual causes can be used to infer transfers of deaths between categories over time, that may cause artifactual changes in mortality rates of particular causes. The data are presented by sex, allowing calculation of sex ratios. The age-specific and annual nature of the dataset allows the analysis of cause-specific mortality by birth cohort (assuming low migration at the national level). The database can be used in conjunction with the ONS database “Historic Mortality and Population Data, 1901-1992”, already in the UK Data Archive collection as SN 2902, to create continuous cause-of-death series for the period 1848-1992 (or later, if using more recent versions of the ONS database).

This document contains supplemental material for the manuscript entitled " Total and regional fat-to-muscle mass ratio in relation to all-cause and cause-specific mortality in men and women" This supplement contains 4 tables and 9 Figures.

Death statistics (i) Number of Deaths for Different Sexes and Crude Death Rate for the Period from 1981 to 2023 (ii) Age-standardised Death Rate (Overall and by Sex) for the Period from 1981 to 2023 (iii) Age-specific Death Rate for Year 2013 and 2023 (iv) Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (v) Number of Deaths by Leading Causes of Death for the Period from 2001 to 2023 (vi) Age-standardised Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (vii) Late Foetal Mortality Rate for the Period from 1981 to 2023 (viii) Perinatal Mortality Rate for the Period from 1981 to 2023 (ix) Neonatal Mortality Rate for the Period from 1981 to 2023 (x) Infant Mortality Rate for the Period from 1981 to 2023 (xi) Number of Maternal Deaths for the Period from 1981 to 2023 (xii) Maternal Mortality Ratio for the Period from 1981 to 2023

Cause-specific mortality rate ratio (MMR) for the childhood obesity cohort compared to the comparison group.

The mortality rate has been stable in France since the middle of 1980s. The mortality rate varies between *** and ***** deaths per 1,000 inhabitants. Life expectancy of women in France amounted to more than 85 years in 2023, making the country one of the areas in Europe where women live the longest. A slowly increasing death rate From 2014 to 2020, the death rate in France generally remained stable, oscillating mostly between *** and *** deaths per 1,000 population. Death rate, also known as mortality rate, is the ratio between the annual number of deaths and the average total population over a given period and on a specific territory. In 2023, the population in France reached ***** million people, while in 2022, the total number of deaths in France was *******. The mortality rate in France increased slowly in recent years. In 2007, the death rate amounted to *** per thousand population, compared to *** deaths ten years later. Causes of death In 2013, the leading cause of death among French citizens was cancer. That year, ******* people died of tumors, while diseases of the circulatory system were the second most common cause of death in the country. Mortality rate because of cancer was particularly high among French males, whereas females appear to be more affected by cardiovascular disease. Studies have shown that cancer was not only the leading cause of death in France, but also in Europe. More broadly, health and diseases were among the major causes of death in European countries, even if traffic accidents killed more than ***** individuals in France in 2021.

BR: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 16.000 Ratio in 2023. This records a decrease from the previous number of 16.200 Ratio for 2022. BR: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 64.300 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 182.300 Ratio in 1960 and a record low of 16.000 Ratio in 2023. BR: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

ObjectiveMore T1N0M0 renal cell carcinoma (RCC) is detected and the prognosis has improved, but, the current focus on non-RCC-related mortality is superficial. We investigated cause-specific mortality and its temporal patterns after an RCC diagnosis.MethodsIn the Surveillance, Epidemiology, and End Results-18 database, patients with T1N0M0 RCC treated with partial nephrectomy (PN) or radical nephrectomy (RN) during 2000–15 were identified. Standardized mortality ratios (SMRs) for cause of death were calculated. Risk predictors for each cause-specific mortality were investigated using the Fine and Gray sub-distribution model.ResultsIn all, 68,612 eligible patients were pooled. A total of 14,047 (20.5%) patients had died (cardiovascular disease [CVD], 28.3%; other non-cancer-related diseases, 20.3%; RCC, 18.7%; other cancer types, 16.3%; non-disease events, 16.1%) during follow-up. Heart disease, diabetes mellitus, and cerebrovascular disease were the primary causes of non-RCC-related mortality within 1 year after the diagnosis. The greatest proportion of death (39.0%) occurred within 1–5 years after the diagnosis, mostly due to RCC itself, followed by heart disease. However, >5 years after the diagnosis, heart disease became the leading cause of death. Compared with the general US population, a 21% (SMR, 1.21; 95%CI 1.19–1.23) increased risk of all-mortality was observed; RCC patients had a higher risk of heart disease-related death within 5–10 years (SMR, 1.10; 95%CI 1.04–1.17) and >10 years (1.12; 1.02–1.22) after the diagnosis. Older age and RN increased the death risk of CVD and RCC-specific mortality. Although a larger tumor diameter increased the risk of RCC-specific death, this was not a significant predictor for CVD. Moreover, for T1N0M0 RCC tumors of diameter >4 cm, there was no significant difference in CVD incidence for RN vs. PN.ConclusionsRCC-specific mortality is a common challenge for the prognosis. Importantly, a large proportion and higher SMRs of other non-RCC-related diseases (especially CVD) should not be disregarded for the better holistic management of survivors of local RCC. Targeted prevention strategies for non-RCC-related death could lead to significant reductions in mortality for RCC survivors.

In 2020, India's case fatality ratio for dengue was ****. In other words, *** percent of the total cases reported were highly severe, frequently leading to death. Interestingly, the fatality rate decreased in the last few years.

This data shows premature deaths (Age under 75), numbers and rates by gender, as 3-year moving-averages.

All-Cause Mortality rates are a summary indicator of population health status. All-cause mortality is related to Life Expectancy, and both may be influenced by health inequalities.

Directly Age-Standardised Rates (DASR) are shown in the data (where numbers are sufficient) so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator ID 108. This data is updated annually.

Indicator 3.4.1Mortality rate attributed to Cardiovascular disease, cancer, diabetes, or chronic respiratory disease.Methodology:There are 4 steps involved in the calculation of this indicator:1. Estimation of life table of the country population or estimation of WHO life tables, based on the UN World Population Prospects 2012 revision.2. Cause-of-death distributions by five years age groups and mid-year population distribution by five years age groups .3. Calculation of age-specific mortality rates from the four main NCDs for each five-year age range between 30 and 70.4. Calculation of the probability of dying between the ages of 30 and 70 years from cardiovascular diseases, cancer, diabetes or chronic respiratory diseases.Finally, converting the probabilities of dying into percentages.Data Source:Ministry of Public Health - Accounts of the National Planning Council.

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

The ratio of deaths directly from COVID-19 to the total number of the disease cases in Moscow was measured at two percent as of May 31, 2020. Taking into account lethal cases where COVID-19 was an accompanying cause of death, the case fatality rate reached 3.8 percent.

Importance: Obesity and metabolic disorders have been associated with an increased risk of cancer and with poorer outcomes in many cohorts of breast cancer (BC) patients, with poor evidence from Mediterranean cohorts.
Objective: To investigate the prognostic potential of anthropometric variables, namely body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), as well as Metabolic Syndrome (MetS) and its components, in early BC patients living in a Southern Mediterranean region of Italy.
Design: Prospective cohort study enrolling consecutive early BC patients who were treated between January 2009 and December 2013 in Southern Italy. Median follow-up was 11.8 years and ended on June 15th 2022. Physicians who measured the study variables were blinded to patient groupings.
Setting: Multicenter study enrolling consecutive, early BC patients referred to specialized cancer centers.
Participants: A total of 955 early BC patients consecutively treated at the Istituto Nazionale dei Tumori, “G. Pascale” and at the Policlinico University Hospital “Federico II”, Naples, Italy, were enrolled. All study subjects provided written informed consent to participate.
Intervention(s) (for clinical trials) and exposure (for observational studies): Anthropometric measurements and indices (BMI, hip circumference and WC) were collected. MetS was defined according to NCEP-ATP III criteria. MetS components were categorized as 0, 1-2 or ≥3.
Main Outcomes and Measures: Overall survival and BC-specific survival.
Results: Mean age was 55.3 years (±12.5 years); 61% of patients were post-menopausal. At the end of follow-up, 208 (22%) patients had died, 131 (14%) of whom from BC. Obesity (BMI≥30 kg/m2) was found in 29% of enrolled patients (14% in pre- and 38% in post-menopause); 24% of patients met the criteria for a diagnosis of MetS (7% in pre- and 36% in post-menopause), whereas 1-2 MetS criteria were found in 53% of patients
High WC or WHR were associated with a moderately increased risk of all-cause mortality (WC ≥ 88 cm, HR=1.39, 95%CI: 1.00-1.94; WHR > 0.85, HR=1.62, 95%CI: 1.12-2.37). Furthermore, an increased risk of all-cause mortality was observed with the presence of MetS (HR=1.61, 95%CI: 1.12-2.32). An increased BC-specific mortality risk was found in obese patients (BMI≥30 kg/m2, HR=1.72, 95%CI: 1.06-2.78) and in those with WC ≥88 (HR=1.71, 95%CI: 1.12-2.61). High WHR was also associated with increased risk of BC-specific mortality, both when evaluated as a categorical variable (WHR>0.85, HR=1.80, 95%CI: 1.13-2.86) and as a continuous variable (for each 0.1-U increase in WHR, HR=1.33, 95%CI: 1.08-1.63). The presence of MetS was associated with an 81% increased risk of BC-specific mortality (HR=1.81, 95%CI: 1.51-2.85).
These associations varied according to menopausal status. In particular, in pre-menopausal patients higher BMI was associated with an increased risk of both all-cause and BC-specific mortality (HR=1.43 and HR=1.58, respectively). In post-menopausal women an increased risk of all-cause mortality was found only in the presence of ≥3 MetS components (HR=2.77, 95%CI: 1.09-7.06). The associations among anthropometric variables and all-cause and BC-specific mortality also varied according to BC subtype. Triple negative BC was the only disease subtype that wasn’t independently associated with BMI, WC, WHR or MetS or all-cause and BC-specific mortality.
Conclusions and Relevance: Central obesity and metabolic disorders result in a highly increased risk of BC death. The magnitude of this effect suggests that obesity may nullify the benefit of effective BC therapies. Active lifestyle interventions to maintain optimal body weight and to prevent MetS should be recommended for several expected beneficial effects, including a potential reduction in BC-specific mortality.

Deaths from alcohol-related conditions, all ages, directly age-standardised rate per 100,000 population (standardised to the European standard population).

Rationale Alcohol consumption is a contributing factor to hospital admissions and deaths from a diverse range of conditions. Alcohol misuse is estimated to cost the NHS about £3.5 billion per year and society as a whole £21 billion annually.

The Government has said that everyone has a role to play in reducing the harmful use of alcohol - this indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related deaths can be reduced through local interventions to reduce alcohol misuse and harm.

The proportion of disease attributable to alcohol (alcohol attributable fraction) is calculated using a relative risk (a fraction between 0 and 1) specific to each disease, age group, and sex combined with the prevalence of alcohol consumption in the population. All mortality records are extracted that contain an attributable disease and the age and sex-specific fraction applied. The results are summed into quinary age bands for the numerator and a directly standardised rate calculated using the European Standard Population. This revised indicator uses updated alcohol attributable fractions, based on new relative risks from ‘Alcohol-attributable fractions for England: an update’ (1) published by PHE in 2020. A detailed comparison between the 2013 and 2020 alcohol attributable fractions is available in Appendix 3 of the PHE report (2). A consultation was also undertaken with stakeholders where the impact of the new methodology on the LAPE indicators was quantified and explored (3).

The calculation that underlies all alcohol-related indicators has been updated to take account of the latest academic evidence and more recent alcohol-consumption figures. The result has been that the newly published mortality and admission rates are lower than those previously published. This is due to a change in methodology, mainly because alcohol consumption across the population has reduced since 2010. Therefore, the number of deaths and hospital admissions that we attribute to alcohol has reduced because in general people are drinking less today than they were when the original calculation was made.

Figures published previously did not misrepresent the burden of alcohol based on the previous evidence – the methodology used in this update is as close as sources and data allow to the original method. Though the number of deaths and admissions attributed to alcohol each year has reduced, the direction of trend and the key inequalities due to alcohol harm remain the same. Alcohol remains a significant burden on the health of the population and the harm alcohol causes to individuals remains unchanged.

References:

PHE (2020) Alcohol-attributable fractions for England: an update PHE (2020) Alcohol-attributable fractions for England: an update: Appendix 3 PHE (2021) Proposed changes for calculating alcohol-related mortality

Definition of numerator Deaths from alcohol-related conditions based on underlying cause of death, registered in the calendar year for all ages. Each alcohol-related death is assigned an alcohol attributable fraction based on underlying cause of death (and all cause of deaths fields for the conditions: ethanol poisoning, methanol poisoning, toxic effect of alcohol). Alcohol-attributable fractions were not available for children.

Mortality data includes all deaths registered in the calendar year where the local authority of usual residence of the deceased is one of the English geographies and an alcohol attributable diagnosis is given as the underlying cause of death. Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: MUSE implementation guidance.

Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: IRIS implementation guidance.

Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change, and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change, and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at: 2011 implementation guidance.

Definition of denominator ONS mid-year population estimates aggregated into quinary age bands.

Caveats There is the potential for the underlying cause of death to be incorrectly attributed on the death certificate and the cause of death misclassified. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator.

The confidence intervals do not take into account the uncertainty involved in the calculation of the AAFs – that is, the proportion of deaths that are caused by alcohol and the alcohol consumption prevalence that are included in the AAF formula are only an estimate and so include uncertainty. The confidence intervals published here are based only on the observed number of deaths and do not account for this uncertainty in the calculation of attributable fraction - as such the intervals may be too narrow.

Belgian National Burden of Disease Study

Estimates of the burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

Prevalence

Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.

Years Lived with Disability

In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

Disability-Adjusted Life Years

Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.

Hazard ratio for cause specific mortality associated with level of physical activity the Hordaland Health Study.