Key Social Determinants to Narrow the Gap between Health-adjusted Life Expectancy and Life Expectancy in Megacities

ObjectiveImprovement in the quality of life is reflected in the narrowing of the gap between health-adjusted life expectancy (HALE) and life expectancy (LE). The effect of megacity expansion on narrowing the gap is rarely reported. This study aimed to disclose this potential relationship.MethodsAnnual life tables were constructed from identified death records and population counts from multiple administrative sources in Guangzhou, China, from 2010 to 2020. Joinpoint regression was used to evaluate the temporal trend. Generalized principal component analysis and multilevel models were applied to examine the county-level association between the gap and social determinants.ResultsAlthough LE and HALE in megacities are increasing steadily, their gap is widening. Socio-economic and health services are guaranteed to narrow this gap. Increasing personal wealth, a growing number of newborns and healthy immigrants, high urbanization, and healthy aging have helped in narrowing this gap.ConclusionIn megacities, parallel LE and HALE growth should be highly considered to narrow their gap. Multiple social determinants need to be integrated as a whole to formulate public health plans.     

Death rates at specific life stages mold the sex gap in life expectancy

Why do women live longer than men? Here, we mine rich lodes of demographic data to reveal that lower female mortality at particular ages is decisive—and that the important ages changed around 1950. Earlier, excess mortality among baby boys was crucial; afterward, the gap largely resulted from elevated mortality among men 60+. Young males bear modest responsibility for the sex gap in life expectancy: Depending on the country and time, their mortality accounts for less than a quarter and often less than a 10th of the gap. Understanding the impact on life expectancy of differences between male and female risks of death by age, over time, and across populations yields insights for research on how the lives of men and women differ.

Between ages 15 and 40, death rates for men are usually two or three times higher than death rates for women. This disparity has fueled widespread interest in the ratio of male to female death rates over the life course and in why it is exceptionally high for younger adults (1–6). Between ages 15 and 40, however, numbers of deaths are relatively low, so the high ratio of male to female death rates has a modest impact on the gap between female and male life expectancies. The sex difference in life expectancy hinges on differences in mortality risks at the ages when deaths are relatively common (7). Up through the early decades of the 20th century, these ages were at both extremes of life, infancy and old age. Afterward, death mostly struck after age 60. Here, we investigate variation across populations, over time, and over the life course in absolute and relative differences in mortality for men and women. We discuss what insights can be gained by scrutinizing relative risks compared to what can be learned by analyzing absolute risks.     

Inequalities in life expectancy by educational level and its decomposition in Barcelona, 2004-2018

ObjectiveTo analyse the gap in life expectancy by educational level in the city of Barcelona from 2004 to 2018 and to decompose this gap by age and causes of death.MethodWe computed abridged life tables at the age of 25 years by sex from 2004 to 2018 using standard methods. Educational level was categorised in two groups (lower secondary or less vs. upper secondary or higher education). The life expectancy gap was further decomposed by age and by causes of death based in Arriaga's method in 5-year age blocks up to the age of ≥ 90 years and broad causes of death using ICD-10 codes.ResultsThe life expectancy gap at 25 years by educational level oscillated without trend at around 3.08 years for men and 1.93 years for women. Decomposition by age showed a favourable significant shift in the contribution to this gap from young to older ages for men, with few changes for women. Decomposition by causes of death showed that the diseases concentrating the largest share of the contribution were neoplasms and respiratory and circulatory disease. There was a significant downward trend in external causes for men and in infectious diseases for both men and women but a significant upward trend for respiratory disease for both sexes.ConclusionsThe stability of the life expectancy gap by educational level during the period analysed resulted from a combination of divergent trends by age and causes of death among high and low educational levels.     

Supervivencia de los pacientes ancianos con implante percutáneo de válvula aórtica comparados con la población general

Introduction and objectivesSevere symptomatic aortic stenosis carries a very poor prognosis. Transcatheter aortic valve replacement has been demonstrated to change the natural history of the disease. However, it is not known whether the probability of survival in older patients receiving this treatment returns to a similar value to that in the general population. Our objective was to determine survival in these patients vs that in the general population.MethodsWe retrospectively analyzed the survival curves of patients older than 75 years who underwent transcatheter aortic valve implantation (TAVI) at our hospital and compared them with those in the general population of the same age, sex, and geographic region by using data from the Spanish National Institute of Statistics.ResultsWe analyzed 526 patients. Among postoperative survivors, survival curves were similar between the 2 groups during most of the follow-up. In TAVI patients, the probability of survival at 1, 3, 5, and 8 years of follow-up was 90.58% (confidence interval [CI] 95%, 87.54-92.91), 72.51% (95%CI, 67.38-76.97), 53.23% (95%CI, 46.52-59.48), and 35.73% (95%CI, 27.72-43.80). In the reference population, these percentages were 91.93%, 75.63%, 59.6%, and 37.47%.ConclusionsLong-term survival in elderly patients undergoing TAVI is influenced by postoperative mortality. In patients surviving the postoperative period, the probability of survival returns to a similar value to that in the general population of the same age, sex, and geographical area.     

2015-2018年乌海市市区居民死因分析

目的 了解2015-2018年乌海市市区居民主要死因及寿命损失，为提出有针对性的疾病预防控制策略提供依据。 方法 整理2015-2018年乌海市市区居民全死因数据，分析死亡率、死因顺位、期望寿命等指标。结果 2015-2018年乌海市市区居民粗死亡率为533.59/10万，标化死亡率为531.61/10万，男性高于女性，随年龄增长死亡率呈升高趋势（〖XC小五号.EPS;P〗=30785.35，P<0.001）。全人群死因前5位依次为循环系统疾病、肿瘤、呼吸系统疾病、损伤和中毒及其他，占全部死亡人数的90.34%；其中循环系统疾病和肿瘤占全部死亡人数的69.33%。2015-2018年乌海市市区居民期望寿命为77.20岁，去循环系统疾病和去肿瘤后期望寿命可分别增加5.44岁和3.33岁。结论 循环系统疾病和肿瘤是乌海市市区居民的两大死因，也是造成居民期望寿命损失最主要的两大疾病，尤其要重点关注肿瘤。     

Internalizing Risk Factors for College Students' Alcohol use: A Combined Person- and Variable-Centered Approach

Background: Most studies that investigate internalizing problems (i.e., depression and anxiety symptoms) and alcohol use disorders use variable-centered approaches, losing important information about differences among individuals. Objectives: To group college students by different profiles of alcohol-use risk factors using a person-centered cluster analysis in two separate samples. Methods: Questionnaires were used in both studies to assess positive expectancies regarding alcohol use, coping motives for alcohol use, and symptoms of depression and anxiety. In the first study (2012), we collected information about past month alcohol use, including frequency and binge drinking episode (n = 171). In the second study (2013), we also included measures of externalizing behaviors and negative alcohol-related consequences (n = 526). Results: In Study 1, the cluster analysis identified four groups of students who displayed different patterns of risk: a low-risk group, moderate cognitions/low internalizing cluster, a high internalizing/low coping motives group of drinkers, and a high internalizing/high coping motives cluster of drinkers. This fourth group showed high levels of depression, moderate anxiety, high positive expectancies and coping motives for alcohol use, and reported the highest frequency of alcohol use. Study 2 replicated the findings from the previous study. Three groups of individuals were identified, replicating the low-risk cluster, the moderate cognitions/low internalizing cluster, and the internalizing cluster of drinkers from Study 1. Participants in the latter cluster endorsed the highest number of negative consequences of alcohol use. Conclusions: Results from both studies highlight the importance of tailoring alcohol abuse prevention efforts to a subgroup young adult who endorse internalizing symptoms.     

The “Jay-Dar” Phenomenon: Individuals Discriminate Cannabis Users from Nonusers Based Upon a Photograph

Objective: With increasing legalization of medicinal and recreational cannabis, use is on the rise. Research suggests individuals may be able to guess cannabis user status based upon appearance; however, these findings utilized a small sample of photographs that was not balanced on user status or gender. Further, no studies examined whether raters with cannabis experience are better at judging others' cannabis use, or what physical features they use to make these judgments. This study explored these factors using a larger, balanced photograph database. Method: An American sample (n = 249, 48.6% female, mean age = 35.19 years) rated 36 photographs (18 cannabis users, 18 nonusers) balanced on gender and age on the likelihood that the photographed individuals use cannabis, producing 8964 ratings. Respondents also reported physical features considered in their ratings, as well as their own cannabis use history. Results: As hypothesized, photographs of users received higher ratings on the Marijuana Use Likelihood Index relative to nonusers. Further, results revealed a gender by rater user status interaction, indicating that raters with no previous cannabis experience rated males higher than females, while raters with cannabis experience did not demonstrate this rating discrepancy. Cannabis use explained over 9% of the variance in ratings across all photographs. Conclusions: Results suggest individuals do rate cannabis users as more likely to be users, relative to nonusers, based upon appearance alone. These findings have important implications, not only for research on chronic cannabis use effects, but also for social and achievement factors such as potential stigma.     

Associations of multiple chronic health conditions with active life expectancy in the United States

Purpose: To estimate associations of eight common health conditions with life expectancy (LE) and disabled life expectancy (DLE), the percentage of life disabled in an activity of daily living. Methods: Data from the Panel Study of Income Dynamics represented Americans ages 55+ (1999–2011, n?=?2118, mean baseline age 63.3, 19?447 person-years). We estimated probabilities of death and disability with multinomial logistic Markov models adjusted for age, sex, race/ethnicity and education. We used the probabilities to create large populations with microsimulations, each individual having a known monthly disability status, age 55 through death. We calculated LE and DLE for the populations, repeating each microsimulation 100 times for confidence intervals. Results: Nearly half (48.8%) of the participants had two or more conditions, 24.7% had three or more, 11.5% had four or more. Having any one condition significantly reduced LE. For example, white women lived to age 87.3 (95% confidence interval 86.5–88.1) with no conditions, 75.8 (70.9–80.7) with heart disease. Multiple conditions did not further reduce LE but often increased DLE, which for white women was 12.2% (11.1–13.2) with no conditions, 39.1% (28.3–49.8) with heart disease and 47.0% (46.9–47.1) with heart disease, diabetes and hypertension. Conclusion: The increasing prevalence of multiple chronic conditions may substantially increase disability.

• Implications for Rehabilitation

• The growing number of individuals with multiple chronic conditions will greatly increase the prevalence of disability in later life.

• It is important for rehabilitation science, practice and policy to address this emerging epidemiological transition.

• Rehabilitation is especially important for people with pre-diabetes, developing heart disease or early stages of other cardiovascular-related diseases as avoiding the development of multiple chronic diseases through increased activity may greatly reduce disability and mortality.