Number of deaths, crude mortality rates and age standardized mortality rates (based on 2011 population) for selected grouped causes, by sex. Data are available beginning from 2000.

Rate and ratio data are given as percent (95% CI).aStandardized by age group, sex, education, occupational attainment, household assets, and food insecurity, using the whole sample as the standard population.bStandardized for age group and sex to US national rates for 2005.

a Direct age standardised mortality rates (deaths per 100,000 person-years).b Results are corrected for exclusion of economically inactive.c Both non-manual categories combined.d Both manual categories combined.Distribution of economically active men (%), age-standardised mortality rate (ASMR)a and rate ratio (RR) of all-cause and cause-specific mortality by occupational class, men, age 30–59b.

The probability of dying between birth and the exact age of 1, expressed per 1,000 live births. The data is sorted by both sex and total and includes a range of values from 1900 to 2019. The calculation for infant mortality rates is derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data. This data is sourced from the UN Inter-Agency Group for Child Mortality Estimation. The UN IGME uses the same estimation method across all countries to arrive at a smooth trend curve of age-specific mortality rates. The estimates are based on high quality nationally representative data including statistics from civil registration systems, results from household surveys, and censuses. The child mortality estimates are produced in conjunction with national level agencies such as a country’s Ministry of Health, National Statistics Office, or other relevant agencies.

Comoros KM: Suicide Mortality Rate: per 100,000 Population data was reported at 6.000 Ratio in 2021. This records a decrease from the previous number of 6.210 Ratio for 2020. Comoros KM: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 6.185 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 6.890 Ratio in 2000 and a record low of 5.780 Ratio in 2009. Comoros KM: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: Social: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

BackgroundPatients with atrial fibrillation are known to have a high risk of mortality. There is a paucity of population-based studies about the impact of atrial fibrillation on the mortality risk stratified by age, sex, and detailed causes of death.MethodsA total of 15,411 patients with atrial fibrillation from the Korean National Health Insurance Service-National Sample Cohort were enrolled, and causes of death were identified according to codes of the 10th revision of the International Classification of Diseases.ResultsFrom 2002 to 2013, a total of 4,479 (29%) deaths were confirmed, and the crude mortality rate for all-cause death was 63.3 per 1,000 patient-years. Patients with atrial fibrillation had a 3.7-fold increased risk of all-cause death compared with the general population. The standardized mortality ratio for all-cause death was the highest in young patients and decreased with increasing age (standardized mortality ratio 21.93, 95% confidence interval 7.60–26.26 in patients aged

Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available.

【リソース】Volume 1_5-1_Trends in deaths, death rates (per 1,000 population) by sex and sex ratio:Japan / / Volume 1_5-2_Trends in crude death rates and age-adjusted death rates (per 1,000 population) by sex:Japan / Volume 1_5-3_International comparison of crude death rates and age-standardized death rates (per 100,000 population), 2008 / Volume 1_5-4_Trends in deaths and death rates (per 1,000 population) by month:Japan / Volume 1_5-5_Trends in deaths by place of occurrence:Japan / Volume 1_5-6_Trends in percent distribution of deaths by place of occurrence:Japan / Volume 1_5-7_Deaths by place of occurrence:Japan, each prefecture and 21 major cities, 2013 / Volume 1_5-8_Percent distribution of deaths by place of occurrence:Japan, each prefecture and 21 major cities, 2013 / Volume 1_5-9_Trends in deaths by each prefecture:Japan / Volume 1_5-10_Trends in death rates (per 1,000 population) by each prefecture:Japan / Volume 1_5-11_Trends in leading causes of death:Japan / Volume 1_5-12_Trends in deaths and death rates (per 100,000 population) by sex and causes of death:Japan / Volume 1_5-13_Trends in deaths and death rates (per 100,000 population) by sex and causes (the condensed list of causes of death for Japan):Japan / Volume 1_5-14_Trends in age-adjusted death rates (per 100,000 population) by sex and causes of death:Japan / Volume 1_5-15_Trends in deaths and death rates (per 100,000 population) by sex, age and causes of death:Japan / Volume 1_5-16_Death rates (per 100,000 population) by sex, age and causes (the condensed list of causes of death for Japan):Japan, 2013 / Volume 1_5-17_Leading causes of death by sex and age:Japan, 2013 / Volume 1_5-18_Death rates (per 100,000 population) by causes (the condensed list of causes of death for Japan) by month:Japan, 2013 / Volume 1_5-19_Death rates (per 100,000 population) by causes (the condensed list of causes of death for Japan):Japan, each prefecture and 21 major cities, 2013 / Volume 1_5-20_Leading causes of death:Japan, each prefecture and 21 major cities, 2013 / Volume 1_5-21_Trends in deaths and percent distribution from leading causes of death by sex and place of occurrence:Japan / Volume 1_5-22_Deaths and percent distribution from leading causes of death by sex, age and place of occurrence:Japan, 2013 / Volume 1_5-23_Deaths and percent distribution by causes (the selected list of causes of death for Japan) and type of occupation of household:Japan, 2013 / Volume 1_5-24_Trends in deaths and death rates (per 100,000 population) from malignant neoplasms by sex and site:Japan / Volume 1_5-25_Trends in death rates (per 100,000 population) from malignant neoplasms by sex, age and site:Japan / Volume 1_5-26_Trends in age-adjusted death rates (per 100,000 population) from malignant neoplasms by sex and site:Japan / Volume 1_5-27_Trends in deaths, percent distribution, crude death rates and age-adjusted death rates (per 100,000 population) from cerebrovascular diseases by sex and disease type:Japan / Volume 1_5-28_Trends in deaths, percent distribution, crude death rates and age-adjusted death rates (per 100,000 population) from heart diseases by sex and disease type:Japan / Volume 1_5-29_Trends in deaths and death rates (per 100,000 population) by causes(the list of infectious diseases):Japan / Volume 1_5-30_Trends in deaths and death rates (per 100,000 population) from accidents by external causes:Japan / Volume 1_5-31_Deaths from accidents by age and external causes:Japan, 2013 / Volume 1_5-32_Percent distribution of deaths from accidents by age and external causes:Japan, 2013 / Volume 1_5-33_Trends in deaths and percent distribution from transportation accidents by external causes:Japan / Volume 1_5-34_Deaths and percent distribution from nontransportation accidents by age and place of occurrence:Japan, 2013 / Volume 1_5-35_Deaths and percent distribution from accidents at home by age and external causes:Japan, 2013 / Volume 1_5-36_Trends in deaths and percent distribution from suicide by sex and external causes:Japan / Volume 1_5-37_Trends in maternal deaths and maternal mortality rates (per 100,000 total births) by causes of death:Japan / Volume 1_5-38_Trends in late maternal deaths and late maternal mortality rates (per 100,000 total births) by causes of death:Japan / Volume 1_5-39_Trends in maternal deaths and maternal mortality rates (per 100,000 total births) by each prefecture:Japan / Volume 2_1_Deaths, infant deaths (under 1 year), neonatal deaths (under 4 weeks) and early neonatal deaths (under 1 week), by place of occurrence, for urban/rural residence:Japan, each prefecture and 21 major cities / Volume 2_2_Deaths by sex and month of occurrence:Japan, urban/rural residence, each prefecture and 21 major cities / Volume 2_3_Deaths by sex and age:Japan, each prefecture and 21 major cities / Volume 2_4_Deaths by sex and single years of age:Japan, each prefecture and 21 major cities_(1) Total,0-64years / Volume 2_4_Deaths by sex and single years of age:Ja

Brazil BR: Suicide Mortality Rate: per 100,000 Population data was reported at 7.590 Ratio in 2021. This records an increase from the previous number of 6.930 Ratio for 2020. Brazil BR: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 5.450 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 7.590 Ratio in 2021 and a record low of 4.230 Ratio in 2000. Brazil BR: Suicide Mortality Rate: per 100,000 Population 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. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

Belarus BY: Suicide Mortality Rate: per 100,000 Population data was reported at 15.610 Ratio in 2021. This records a decrease from the previous number of 16.270 Ratio for 2020. Belarus BY: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 34.105 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 40.520 Ratio in 2005 and a record low of 15.610 Ratio in 2021. Belarus BY: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

BackgroundPediatric obesity is associated with increased risk of premature death from middle age onward, but whether the risk is already increased in young adulthood is unclear. The aim was to investigate whether individuals who had obesity in childhood have an increased mortality risk in young adulthood, compared with a population-based comparison group.Methods and findingsIn this prospective cohort study, we linked nationwide registers and collected data on 41,359 individuals. Individuals enrolled at age 3–17.9 years in the Swedish Childhood Obesity Treatment Register (BORIS) and living in Sweden on their 18th birthday (start of follow-up) were included. A comparison group was matched by year of birth, sex, and area of residence. We analyzed all-cause mortality and cause-specific mortality using Cox proportional hazards models, adjusted according to group, sex, Nordic origin, and parental socioeconomic status (SES). Over 190,752 person-years of follow-up (median follow-up time 3.6 years), 104 deaths were recorded. Median (IQR) age at death was 22.0 (20.0–24.5) years. In the childhood obesity cohort, 0.55% (n = 39) died during the follow-up period, compared to 0.19% (n = 65) in the comparison group (p < 0.001). More than a quarter of the deaths among individuals in the childhood obesity cohort had obesity recorded as a primary or contributing cause of death. Male sex and low parental SES were associated with premature all-cause mortality. Suicide and self-harm with undetermined intent were the main cause of death in both groups. The largest difference between the groups lay within endogenous causes of death, where children who had undergone obesity treatment had an adjusted mortality rate ratio of 4.04 (95% CI 2.00–8.17, p < 0.001) compared with the comparison group. The main study limitation was the lack of anthropometric data in the comparison group.ConclusionsOur study shows that the risk of mortality in early adulthood may be higher for individuals who had obesity in childhood compared to a population-based comparison group.

BackgroundTo examine explanations for the higher rates of male mortality in two Scottish cohorts compared with a cohort in south-east England for which similar data were collected. Methodology/Principal FindingsWe compared three cohort studies which recruited participants in the late 1960s and early 1970s. A total of 13,884 men aged 45–64 years at recruitment in the Whitehall occupational cohort (south-east England), 3,956 men in the Collaborative occupational cohort and 6,813 men in the Renfrew & Paisley population-based study (both central Scotland) were included in analyses of all-cause and cause-specific mortality. All-cause mortality was 25% (age-adjusted hazard ratio 1.25, 95% confidence interval (CI)1.21 to 1.30) and 41% (hazard ratio 1.41 (95% CI 1.36 to 1.45) higher in the Collaborative and Renfrew & Paisley cohorts respectively compared to the Whitehall cohort. The higher mortality rates were substantially attenuated by social class (to 8% and 17% higher respectively), and were effectively eliminated upon the further addition of the other baseline risk factors, such as smoking habit, lung function and pre-existing self-reported morbidity. Despite this, coronary heart disease mortality remained 11% and 16% higher, stroke mortality 45% and 37% higher, mortality from accidents and suicide 51% and 70% higher, and alcohol-related mortality 46% and 73% higher in the Collaborative and Renfrew & Paisley cohorts respectively compared with the Whitehall cohort in the fully adjusted model. Conclusions/SignificanceThe higher all-cause, respiratory, and lung cancer male mortality in the Scottish cohorts was almost entirely explained by social class differences and higher prevalence of known risk factors, but reasons for the excess mortality from stroke, alcohol-related causes, accidents and suicide remained unknown.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Chile CL: Suicide Mortality Rate: per 100,000 Population data was reported at 7.690 Ratio in 2021. This records a decrease from the previous number of 8.260 Ratio for 2020. Chile CL: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 10.195 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 12.760 Ratio in 2009 and a record low of 7.690 Ratio in 2021. Chile CL: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

Note: MRR = Mortality Rate Ratio; CI = confidence interval; AIC = Akaike Information Criteria; RL = Relative Likelihood.Among ≥ 25 years of age. Race-specific age and sex-adjusted mortality rates weighted using the US 2000 standard population per 100,000 person-years from death certificates and mid-year population counts collated by the National Center for Health Statistics, 2004–2009. Area characteristics at the designated market area level from the American Community Survey, 2004–2009 for urbanicity (% living in a city with ≥ 50,000 people); % Black; education among Blacks (% with up to high school education); and poverty among Blacks (% households in poverty). All models adjusted for individual age, sex, year of death, and Census region.Nested negative binomial regression models estimating associations with Black all-cause mortality rates.

Note: Among ≥ 25 years of age. Area characteristics at the designated market area level from the American Community Survey, 2004–2009 for urbanicity (% living in a city with ≥ 50,000 people); % Black; education among Blacks (% with up to high school education); and poverty among Blacks (% households in poverty). Race-specific age and sex-adjusted mortality rates weighted using the US 2000 standard population per 100,000 person-years from death certificates and mid-year population counts collated by the National Center for Health Statistics, 2004–2009.Descriptive characteristics.

Central African Republic CF: Suicide Mortality Rate: per 100,000 Population data was reported at 9.190 Ratio in 2021. This records a decrease from the previous number of 9.730 Ratio for 2020. Central African Republic CF: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 10.705 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 12.610 Ratio in 2000 and a record low of 9.190 Ratio in 2021. Central African Republic CF: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

Adjusted Hazard ratio for stroke mortality by quartiles of SD of serum uric acid variability.

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