Geographical Inequalities in Mortality by Age and Gender in Italy, 2002-2019: Insights from a Spatial Extension of the Lee-Carter Model.

BackgroundA recent study from Japan suggested that geographic inequalities in all-cause premature adult mortality have increased since 1995 in both sexes even after adjusting for individual age and occupation in 47 prefectures. Such variations can arise from compositional effects as well as contextual effects. In this study, we sought to further examine the emerging geographic inequalities in all-cause mortality, by exploring the relative contribution of composition and context in each prefecture.MethodsWe used the 2005 vital statistics and census data among those aged 25 or older. The total number of decedents was 524,785 men and 455,863 women. We estimated gender-specific two-level logistic regression to model mortality risk as a function of age, occupation, and residence in 47 prefectures. Prefecture-level variance was used as an estimate of geographic inequalities in mortality, and prefectures were ranked by odds ratios (ORs), with the reference being the grand mean of all prefectures (value = 1).ResultsOverall, the degree of geographic inequalities was more pronounced when we did not account for the composition (i.e., age and occupation) in each prefecture. Even after adjusting for the composition, however, substantial differences remained in mortality risk across prefectures with ORs ranging from 0.870 (Okinawa) to 1.190 (Aomori) for men and from 0.864 (Shimane) to 1.132 (Aichi) for women. In some prefectures (e.g., Aomori), adjustment for composition showed little change in ORs, while we observed substantial attenuation in ORs in other prefectures (e.g., Akita). We also observed qualitative changes in some prefectures (e.g., Tokyo). No clear associations were observed between prefecture-level socioeconomic status variables and the risk of mortality in either sex.ConclusionsGeographic disparities in mortality across prefectures are quite substantial and cannot be fully explained by differences in population composition. The relative contribution of composition and context to health inequalities considerably vary across prefectures.

BackgroundRoad traffic accident (RTA) is one of the major public health problems in the world, especially in developing countries. RTAs have become the first leading cause of burden of disease...

Mortality inequalities have been described across Latin American countries, but less is known about inequalities within cities, where most populations live. We aimed to identify geographic and socioeconomic inequalities in mortality within the urban areas of four main cities in Colombia. We analyzed mortality due to non-violent causes of diseases in adults between 2015 and 2019 using census sectors as unit of analysis in Barranquilla, Bogotá, Cali, and Medellín. We calculated smoothed Bayesian mortality rates as main health outcomes and used concentration indexes (CInd) for assessing inequalities using the multidimensional poverty index (MPI) as the socioeconomic measure. Moran eigenvector spatial filters were calculated to capture the spatial patterns of mortality and then used in multivariable models of the association between mortality rates and quintiles of MPI. Social inequalities were evident but not consistent across cities. The most disadvantaged groups showed the highest mortality rates in Cali. Geographic inequalities in mortality rates, regardless of the adults and poverty distribution, were identified in each city, suggesting that other social, environmental, or individual conditions are impacting the spatial distribution of mortality rates within the four cities.

BackgroundLarge cities are often claimed to display more distinct geographical and socioeconomic health inequalities than other areas due to increasing residential differentiation. Our aim was to assess whether geographical inequalities in mortality within the capital (City of Helsinki) both exceeded that in other types of geographical areas in Finland, and whether those differences were dependent on socioeconomic inequalities.MethodsWe analysed the inequality of distribution separately for overall, ischemic heart disease and alcohol-related mortality, and mortality amenable (AM) to health care interventions in 1992–2008 in three types of geographical areas in Finland: City of Helsinki, other large cities, and small towns and rural areas. Mortality data were acquired as secondary data from the Causes of Death statistics from Statistics Finland. The assessment of changing geographical differences over time, that is geographical inequalities, was performed using Gini coefficients. As some of these differences might arise from socioeconomic factors, we assessed socioeconomic differences with concentration indices in parallel to an analysis of geographical differences. To conclude the analysis, we compared the changes over time of these inequalities between the three geographical areas.ResultsWhile mortality rates mainly decreased, alcohol-related mortality in the lowest income quintile increased. Statistically significant differences over time were found in all mortality groups, varying between geographical areas. Socioeconomic differences existed in all mortality groups and geographical areas. In the study period, geographical differences in mortality remained relatively stable but income differences increased substantially. For instance, the values of concentration indices for AM changed by 54 % in men (p < 0.027) and by 62 % in women (p < 0.016). Only slight differences existed in the time trends of Gini or in the concentration indices between the geographical areas.ConclusionsNo geographical or income-related differences in the distribution of mortality existed between Helsinki and other urban or rural areas of Finland. This suggests that the effect of increasing residential differentiation in the capital may have been mitigated by the policies of positive discrimination and social mixing. One of the main reasons for the increase in health inequalities was growth of alcohol-related mortality, especially among those with the lowest incomes.

Background and Objectives: Road traffic accident (RTA) is one of the major public health problems. Inequality in this problem and its trend has not been yet investigated in Iran. A better understanding of different contributing factors to RTA like inequalities can be helpful to reduce the negative influence of road crashes. Hence, this study aimed at examining geographical inequality in mortality from RTAs over the previous decades. Materials and Methods: In this cross-sectional study, all people who died from RTAs from March 2006 to February 2015 were selected. The data were taken from Legal Medicine Organization in Iran. The Theil index was used to estimate geographical inequality in mortality from RTA. Data were then analyzed using the Stata software. Results: During the 10-year period of this study, 210,582 people in Iran died due to RTA. The mortality rate that caused by RTAs has decreased during the study. We found a larger reduction in a mortality rate in suburban RTAs. In spite of the reduction in RTAs death, geographical inequality increased during the 10-year study. Conclusions: The findings of this study indicate that there is a geographical inequality in mortality from RTAs in Iran. This implies that effective interventions in reducing the RTAs should be distributed equally among regions of the country. Further research can be performed to investigate the causes of this inequality.

Objective To report on the extent of inequality in premature mortality as measured between geographical areas in Britain.Design Observational study of routinely collected mortality data and public records. Population subdivided...

Objective To report on the extent of inequality in premature mortality as measured between geographical areas in Britain. Design Observational study of routinely collected mortality data and public records. Population subdivided by age, sex, and geographical area (parliamentary constituencies from 1991 to2007, pre-1974 local authorities over a longer time span). Setting Great Britain. Participants Entire population aged under 75 from 1990 to 2007, and entire population aged under 65 in the periods 1921-39, 1950-3, 1959-63, 1969-73, and 1981-2007. Main outcome measure Relative index of inequality (RII) and ratios of inequality in age-sex standardised mortality ratios under ages 75 and 65. The relative index of inequality is the relative rate of mortality for the hypothetically worst-off compared with the hypothetically best-off person in the population, assuming a linear association between socioeconomic position and risk of mortality. The ratio of inequality is the ratio of the standardised mortality ratio of the most deprived 10% to the least deprived 10%. Results When measured by the relative index of inequality, geographical inequalities in age-sex standardised rates of mortality below age 75 have increased every two years from 1990-1 to 2006-7 without exception. Over this period the relative index of inequality increased from 1.61 (95% confidence interval 1.52 to 1.69) in 1990-1 to 2.14 (2.02 to 2.27) in 2006-7. Simple ratios indicated a brief period around 2001 when a small reduction in inequality was recorded, but this was quickly reversed and inequalities up to the age of 75 have now reached the highest levels reported since at least 1990. Similarly, inequalities in mortality ratios under the age of 65 improved slightly in the early years of this century but the latest figures surpass the most extreme previously reported. Comparison of crudely age-sex standardised rates for those below age 65 from historical records showed that geographical inequalities in mortality are higher in the most recent decade than in any similar time period for which records are available since at least 1921. Conclusions Inequalities in premature mortality between areas of Britain continued to rise steadily during the first decade of the 21st century. The last time in the long economic record that inequalities were almost as high was in the lead up to the economic crash of 1929 and the economic depression of the 1930s. The economic crash of 2008 might precede even greater inequalities in mortality between areas in Britain.

BackgroundGeographic inequality in US mortality has increased rapidly over the last 25 years, particularly between metropolitan and nonmetropolitan areas. These gaps are sizeable and rival life expectancy differences between the US and other high-income countries. This study determines the contribution of smoking, a key contributor to premature mortality in the US, to geographic inequality in mortality over the past quarter century.MethodsWe used death certificate and census data covering the entire US population aged 50+ between Jan 1, 1990 and Dec 31, 2019. We categorized counties into 40 geographic areas cross-classified by region and metropolitan category. We estimated life expectancy at age 50 and the index of dissimilarity for mortality, a measure of inequality in mortality, with and without smoking for these areas in 1990–1992 and 2017–2019. We estimated the changes in life expectancy levels and percent change in inequality in mortality due to smoking between these periods.ResultsWe find that the gap in life expectany between metros and nonmetros increased by 2.17 years for men and 2.77 years for women. Changes in smoking-related deaths are responsible for 19% and 22% of those increases, respectively. Among the 40 geographic areas, increases in life expectancy driven by changes in smoking ranged from 0.91 to 2.34 years for men while, for women, smoking-related changes ranged from a 0.61-year decline to a 0.45-year improvement. The most favorable trends in years of life lost to smoking tended to be concentrated in large central metros in the South and Midwest, while the least favorable trends occurred in nonmetros in these same regions. Smoking contributed to increases in mortality inequality for men aged 70+, with the contribution ranging from 8 to 24%, and for women aged 50–84, ranging from 14 to 44%.ConclusionsMortality attributable to smoking is declining fastest in large cities and coastal areas and more slowly in nonmetropolitan areas of the US. Increasing geographic inequalities in mortality are partly due to these geographic divergences in smoking patterns over the past several decades. Policies addressing smoking in non-metropolitan areas may reduce geographic inequality in mortality and contribute to future gains in life expectancy.

Twenty years of socioeconomic inequalities in premature mortality in Barcelona: The influence of population and neighbourhood changes

In Ecuador, there are inequalities in the completeness and quality of the mortality registry between men and women and among geographical areas. Consequently, using cause of death statistics leads to several difficulties. Our aim was to analyze geographical inequalities in mortality due to some of the main specific causes of death in the provinces of Ecuador (2001-2016) after correction for the deficiencies found in the mortality registry. This ecologic study used mortality data from 2001 to 2016 for the 22 provinces of Ecuador at the beginning of the study period. We assessed completeness using death distribution methods for the intercensal period 2001-2010. We assessed quality by estimating the percentage of garbage codes for the entire study period. We corrected mortality using completeness as a correction factor and applying a garbage code redistribution protocol. We estimated age-standardized mortality ratios in the provinces of Ecuador for men and women, before and after applying the correction methods. We found substantial changes in the number of deaths due to the selected causes after garbage code redistribution and correction for completeness. These changes corresponded to the deficiencies in completeness and quality found in the study areas and the manner in which garbage codes were redistributed to each of the studied causes. We observed changes in the geographical patterns of mortality due to specific causes. Correcting deficiencies in the mortality registry resulted not only in changes in the number of deaths but also in the geographical patterns of mortality in Ecuador.

Longevity and death rates are closely linked to socioeconomic conditions. However, the relationship between income and specific causes of death (CoD) remains insufficiently explored. This study examines socioeconomic inequalities in cause-specific mortality in Spain, simultaneously accounting for CoD, age, sex, and income. To the best of our knowledge, no previous study has jointly combined all these variables. Using data for the entire population residing in Spain from 2010 to 2019, we compute death rates by CoD, age, sex, and income decile by linking individual demographic records with census tract-level disposable income. The analysis covers 4million deaths and over 466million person-years at risk. Robust income-based relative risks and gradients by CoD are calculated for five-year age groups, stratified by sex, while Relative Index of Inequality (RII) values are derived for single years of age. Results show strong and consistent income-related inequalities in mortality across age, CoD, and sex, especially among younger and middle-aged groups. Particularly steep and significant gradients are found for circulatory, respiratory, digestive, infectious, endocrine, and genitourinary diseases, as well as for symptoms and abnormal findings, and other conditions, with lower-income individuals facing markedly higher risks. For neoplasms, nervous, mental and behavioural disorders, and external causes, patterns are more heterogeneous and gender-specific. Notably, neoplasms exhibit RIIs < 1 among women aged 55 and older. Overall, the results highlight widespread income-related inequalities in mortality in Spain and underscore the need to prioritise lower-income groups in public health efforts, particularly regarding chronic and behavioural health conditions.

BackgroundNational linked mortality and census data have not previously been available for Australia. We estimated education-based mortality inequalities from linked census and mortality data that are suitable for international comparisons.MethodsWe used the Australian Bureau of Statistics Death Registrations to Census file, with data on deaths (2011–2012) linked probabilistically to census data (linkage rate 81%). To assess validity, we compared mortality rates by age group (25–44, 45–64, 65–84 years), sex and area-inequality measures to those based on complete death registration data. We used negative binomial regression to quantify inequalities in all-cause mortality in relation to five levels of education [‘Bachelor degree or higher’ (highest) to ‘no Year 12 and no post-secondary qualification’ (lowest)], separately by sex and age group, adjusting for single year of age and correcting for linkage bias and missing education data.ResultsMortality rates and area-based inequality estimates were comparable to published national estimates. Men aged 25–84 years with the lowest education had age-adjusted mortality rates 2.20 [95% confidence interval (CI): 2.08‒2.33] times those of men with the highest education. Among women, the rate ratio was 1.64 (1.55‒1.74). Rate ratios were 3.87 (3.38‒4.44) in men and 2.57 (2.15‒3.07) in women aged 25–44 years, decreasing to 1.68 (1.60‒1.76) in men and 1.44 (1.36‒1.53) in women aged 65–84 years. Absolute education inequalities increased with age. One in three to four deaths (31%) was associated with less than Bachelor level education.ConclusionsThese linked national data enabled valid estimates of education inequality in mortality suitable for international comparisons. The magnitude of relative inequality is substantial and similar to that reported for other high-income countries.

Geographical inequalities in mortality across Europe may be influenced by migration between regions. The relationship between age- and sex-standardised death rates, 2008-2010, and population change resulting from migration 2000-2010, was analysed in 250 'Nomenclature of Statistical Territorial Units' (NUTS) level 2 regions in 26 European countries. Across Europe death rates were significantly higher in regions experiencing population loss. This association continued after adjustment for 2005 household income among all regions and Western regions but not among Eastern areas. This analysis suggests migration could contribute to Europe's persistent inequalities in mortality, and highlights the problems of Eastern regions with the highest death rates, lowest incomes and declining populations.

In recent years, US life expectancy has stagnated relative to other developed countries and geographic inequalities in mortality within the United States have widened. Much of the recent literature has focused on non-Hispanic White mortality. Less attention has been devoted to non-Hispanic Black mortality independent of Black-White disparities. In this paper, we examine trends in non-Hispanic Black male and female life expectancy between 1990 and 2019 by metropolitan category and region/division, including age-group and cause-of-death contributions to these trends. We document considerable geographic divergence in life expectancy over time with the largest improvements in large central and large fringe metropolitan areas and smallest improvements in small/medium metros and nonmetropolitan areas. We also document sizable differences across regions, with the largest gains in the Northeast and the South Atlantic region and the smallest in other parts of the South and the Midwest. Most gains were achieved by 2010 with stagnating or declining life expectancies thereafter. We find particularly adverse trends in the Midwest after 2010 where Black life expectancies declined in all metro categories. We provide a discussion of the potential explanatory factors and call for greater attention to the study of non-Hispanic Black mortality.

The purpose of this note is to call attention to an interpolation procedure used in Bureau of Census illustrative projections of the number of households from 1960 to 1980.1 The present writer turned to these series in order to estimate annual household formation rates in the 1960's. The data there indicate that the annual number of households formed might be very low in years ending July 1960, and July 1961, and might decline in 1965 and 1966. These figures for Series B, a medium high projection, are in the second column of Table 1. Note that the figures for 1960 and 1961 (less than 600,000) are substantially lower than the average rate in the three years ending March 1959 of about 840,000.2 A different interpolation procedure indicates that annual household formation rates should not be particularly low in 1961 and that the number should rise rather smoothly from 1960 to 1970. Although widespread use has not been made of the annual figures that can be derived from the Bureau of Census publication, mention is made of the low formation rate just prior to 1962 in both an article in Fortune and the new book by Bogue.3 The Bureau procedure here was to project the total number of households that would be in existence in July of the years 1960, 1965, and 1970. Figures for intermediate years obtained through an interpolation process, in which preliminary figures were based on the assumption that the year-to-year growth in number of households would follow the same pattern as the growth in the number of men who are in the principal ages of first marriage (age 20 to 24); the final figures shown in Table 2 resulted from the application of a three-year inoving average to these preliminary figures.4 (Emphasis supplied.) It is the argument of this note that the number of households formed (the growth or first differences of the number of households in existence) is more closely correlated with the number of men age 20 to 24 than with the growth or first differences in the number of men in this age group. The argument is that, other things being equal (particularly the relative portion of men in each single year of age within the group), there will be more marriages out of a large group of men age 20 to 24 than out of a smaller group.5 As a matter of fact, the number of men in this age group increases each year from 1960 to 1970 and, other things remaining equal, we should expect a larger number of marriages in each year.6 (The first diferences in number of men in this age group increase and decrease so widely over this period, that it is not surprising that a three-year moving average had to be applied in the Census interpolations.) Interpolations from 1960 to 1970 based on the number of men in this age group are shown in the third column of Table 1. However, (1) the proportion of men at each year of age in the group 20 to 24 changes over this period and (2) the marriage rate at each single year of age varies greatly over even this small range. Therefore, a better basis for interpolation might be an annual series of estimated first marriages based on s'ingle yeai of age marriage rates, and population by single year of age. In connection with other work, the writer had made annual projections of first marriages and remarriages in the 1960's based on the number of females in each year by single year of age and on marriage rates by single year of age developed by Jacobson for 1948.7 Approximately 66 percent of the total increase in new households from 1950 to 1965 is the increase in husband-wife house-