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Objective: This study aimed to investigate the relationship between education and different indicators of material wealth with mortality, and to analyze whether this relationship varies with the leading causes of death. Methods: All persons aged 65 and older residing in Spain in 2001 were followed up for 7 years to determine their vital status. The relationship between mortality and four indicators of socioeconomic position (education, number of rooms in home, surface area of home and number of vehicles) was estimated in three age groups: 65-74, 75-84 and 85 and older. Rate ratios and relative index of inequality (RII) were calculated for general mortality and for the leading causes of death by Poisson regression. Results: In women, the mortality rate ratio for low vs. high educational level was 1.48 for persons aged 65-74, 1.43 for those aged 75-84 and 1.40 for those aged 85 and older. The respective rates for men were 1.30, 1.25 and 1.29. For the indicators of material wealth, the differences between morality rates in the lower vs. the higher socioeconomic categories decline with age. Mortality differences by the leading causes of death decline with age, except in the case of cancer in women and cardiovascular and digestive mortality in men according to educational level. Conclusions: Relative socioeconomic differences in mortality in the older Spanish population decrease with age using indicators of material wealth but not using educational level. The variation in the pattern of mortality by cause of death by level of education may be responsible for these findings.

Background: Socioeconomic differences in old-age mortality have not been studied in Germany. This study fills in the gap, evaluating mortality and life expectancy differentials among retired German men aged 65+ in 2003. Methods: Mortality rates are calculated from the administrative database on all public pensions and deaths of pensioners in 2003. Relative mortality rates and life expectancies are estimated for population subgroups according to the quintiles of lifetime earnings, type of medical insurance, broad occupational group, and residence in eastern or western Germany. Results: Among pension income quintiles, mortality varies by 60% and life expectancy at age 65 ranges from 14.9 to 18.5 years. Quintile-specific mortality and life-expectancy values are only slightly more favorable in western compared to eastern Germany. The mortality of manual workers is by 35% greater than that of salaried employees, while the mortality of those with mandatory public health insurance is 44% greater than the mortality of those with private or voluntary public health insurance. When all four characteristics are taken into account, relative mortality in the group with the highest mortality is three times higher than at the opposite end of the distribution, and corresponding life expectancies are 12.5 and 20 years. Half of all male deaths at ages 65+ are attributable to this variation. The mortality differentials remain significant at ages 80+. Conclusions: Socioeconomic mortality differentials persist into old age. They are similar in both regions and their magnitude is much greater than the diminishing mortality gap between the two parts of the country.

BACKGROUND: In the late 1980s and early 1990s a generalized HIV epidemic affected Thailand which was relatively well controlled by an intensive national campaign by the mid 1990s. The extent to which the epidemic has slowed or possibly reversed the epidemiological transition in Thailand is relatively unknown. METHODS: Under-five mortality rates (U5MR) were determined from various sources and weighted least squares regression conducted to determine U5MR over the years 1980-2000. Direct and indirect estimates of the completeness of death registration were used to estimate mortality levels in those aged more than 5 years for the 1980-90 and 1990-2000 periods. Life tables were constructed using the various estimates to determine changes in life-expectancy between the two time periods. RESULTS: U5MR in Thailand is estimated to have been 58/1000 live births in 1980, declining to 30 in 1990 and to 23 in 2000. The vital registration system clearly underestimates U5MR. Successive surveys of Population Change (SPC) imply coverage of death registration improving from 75-77% in 1985-86 to 95% in 1995-96, partly due to a reliance on self-reported registration in the latter survey. In contrast, the General Growth Balance-Synthetic Extinction Generations (GGB-SEG) method suggests coverage worsening from 78-85% in 1980-90 to 64-72% in 1990-2000. Life tables based on SPC adjustments show continued declines in female, and to a lesser extent, male adult mortality with corresponding increases in life-expectancy at birth of around 6 years for both sexes from 1980-90 to 1990-2000. In contrast, the indirect adjustments suggest a substantial increase in male adult mortality with female adult mortality unchanged; life expectancy decreased by 4 years for males and was only marginally higher in females. CONCLUSION: Given the conflicting evidence a definitive assessment of mortality change in Thailand between 1980 and 2000 is difficult to make. Indirect adjustments, based on demographic methods point to a major reversal in mortality decline among males, and a slowing in females. If adult mortality registration has declined, and given the continued under-registration of infant and child deaths, remedial measures are urgently required if the mortality system is to better inform and monitor health development in Thailand.

Background. Socioeconomic inequalities in mortality have been repeatedly observed in Britain, the United States, and Europe, and in some countries, there is evidence that the differentials are widening. This study describes trends in socioeconomic mortality inequality in Australia for males and females aged 0-14, 15-24 and 25-64 years over the period 1985-87 to 1995-97. Methods. SES was operationalised using the Index of Relative Socioeconomic Disadvantage, an area based measure developed by the Australian Bureau of Statistics. Mortality differentials were examined using age-standardised rates, and mortality inequality was assessed using rate ratios, gini coefficients, and a measure of excess mortality. Results. For both periods, and for each sex/age sub-group, mortality rates were highest in the most disadvantaged areas. The extent and nature of socioeconomic mortality inequality differed for males and females and for each age group: both increases and decreases in mortality inequality were observed, and for some causes, the degree of inequality remained unchanged. If it were possible to reduce death rates among the SES areas to a level equivalent to that of the least disadvantaged area, premature all–cause mortality for males in the three age groups would be lower by 22%, 28% and 26% respectively, and for females, 35%, 70% and 56%. Conclusions. The mortality burden in the Australian population attributable to socioeconomic inequality is large, and has profound and far-reaching implications in terms of the unnecessary loss of life, the loss of potentially economically productive members of society, and increased costs for the health care system. Keywords: Socioeconomic status, mortality inequality, Australia, area-based measures.

The aim was to identify the age-, sex- and cause-specific premature mortality rates contributing to the association between life expectancy and income distribution in developed countries. Income distribution was calculated for the 13 OECD countries and years for which the Luxembourg Income Study held data. The potential years of life lost (1-65 years) by sex and cause, as well as the age- and sex-specific all-cause mortality rates and standardized mortality ratios for children 1-19 years were calculated from data supplied by the WHO. On finding evidence suggesting that reported income distribution is strongly affected by low response rates in some income surveys, we used 2 measures of income distribution: that among households where the 'head of household' was aged less than 65 years (weighted by response rates) and that among households with children (among whom response rates are thought to be higher). Partial correlations and regressions controlling for the year were used to analyse the relationship between mortality and income distribution. Both measures of income distribution showed broadly similar results. A more egalitarian distribution of income was related to lower all-cause mortality rates In both sexes in most age groups. All 6 major categories of cause of death contributed to this relationship. The causes of premature mortality contributing most were road accidents, chronic liver disease and cirrhosis, infections, ischaemic heart disease among women and other injuries among men. Income distribution was associated not only with larger absolute changes in mortality from these causes, but also with larger proportionate changes. Suicides and stomach cancer tended to be more common in more egalitarian countries.

Determining the biological limits to human longevity is more difficult than for most other species because humans are long-lived. Consequently, mortality data, such as from the U.S. vital statistics system, which have been available for a long time (relative to most epidemiological studies) and have large numbers of cases, including deaths reported to advanced ages, are important in studying human longevity — though care must be exercised in dealing with error in age reporting. Furthermore, it is unlikely that free-living humans can realize as much of their biological endowment for longevity as animals living in a highly controlled experimental environment. We examined changes, 1960 to 1990, in U.S. White male and female extinct cohort life tables and age at death distributions to (a) examine evidence for the effects of a biological life span limit in current U.S. mortality patterns and (b) produce lower bound estimates of that limit.

We used the hypothesis of mortality compression as a framework to examine patterns of mortality from 1962 to 1984. Data from national vital statistics records were used for analysis of the changing age at death for percentiles of the population. Data from the Social Security Administration and the U.S. Census Bureau were used to calculate the force of mortality. The mean age at death for all percentiles, including the oldest groups, has risen during the interval. Examination of the coefficient of variation for the mean age at death suggests that there is a relative increase in the variability of age at death among the oldest old. The available data do not fit a hypothetical sequence of normal density distributions with an increasing mean and declining standard deviation. The force of mortality in those over 85 years appears to be decreasing in a pattern similar to that for those under 85 years. Current mortality patterns suggest an "expansion," rather than compression, of mortality at the oldest ages. Further refinement of these observations, with improved data on mortality among the oldest old, will be helpful in delineating mortality patterns.

The infant mortality rate is not a good indicator of overall mortality or health status. Based on new empirical life tables from the UN Population Division, it can only predict life expectancy with 95% confidence to within a 14-year range. Two infant mortality rates must be nearly 80 units apart to be 95% confident that life expectancy in the two communities is different. Life expectancy itself is not an ideal general measure of mortality, because it implicitly weights deaths at different ages in an inconsistent fashion. A measure of potential years of life lost is preferable because it is ethically more consistent.This paper re-assesses the use of the infant mortality rate and the life expectancy at birth as indexes for general mortality in developing countries, and then suggests and alternative, called the linear index of mortality or LIM. The infant mortality rate is not a good indicator of overall mortality or health status because it is sensitive to socioeconomic changes, in particular to maternal education. Based on new empirical life tables from the UN Population Division, it can only predict life expectancy with 95% confidence to within a 14-year range. Life expectancy itself is not an ideal general measure of mortality because it implicitly weights deaths at different ages in a inconsistent way. The linear index of mortality (LIM) is a measure of potential years of life lost. By weighting the number of deaths in each age group by 100 minus the age of death, a composite index of general mortality can be calculated that is linear, uniform and ethically consistent. Using UN empirical life tables for 20 developing countries, it is possible from the LIM to get an idea how much the difference between high and low mortality countries is due to differences in infant, child and adult mortality. This kind of comparison is impossible with life expectancy because it is non-linear. The relationship between the natural log of life expectancy and LIM is linear. Alternative health status measures like LIM need to be developed, in order to make our statistical perception of the world's nations consistent with our ethical conception of how health should be measured.