Inequalities in lifespan and mortality risk in the US, 2015-2019: a cross-sectional analysis of subpopulations by social determinants of health.

OBJECTIVE: To quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups of the population at high risk of mortality. DESIGN: Comparison of life tables across 54 subpopulations defined by combinations of four social determinants of health: sex, marital status, education and race, using data from the Multiple Cause of Death dataset and the American Community Survey. SETTING: United States, 2015-2019. MAIN OUTCOME MEASURES: We compared the partial life expectancies (PLEs) between age 30 and 90 years of all subpopulations. We also developed a scoring system to identify subgroups at high risk of mortality. RESULTS: There is an 18.0-year difference between the subpopulations with the lowest and highest PLE. Differences in PLE between subpopulations are not significant in most pairwise comparisons. We visually illustrate how the PLE changes across social determinants of health. There is a complex interaction among social determinants of health, with no single determinant fully explaining the observed variation in lifespan. The proposed scoring system adds clarification to this interaction by yielding a single score that can be used to identify subgroups that might be at high risk of mortality. A similar scoring system by cause of death was also created to identify which subgroups could be considered at high risk of mortality from specific causes. Even if subgroups have similar mortality levels, they are often subject to different cause-specific mortality risks. CONCLUSIONS: Having one characteristic associated with higher mortality is often not sufficient to be considered at high risk of mortality, but the risk increases with the number of such characteristics. Reducing inequalities is vital for societies, and better identifying individuals and subgroups at high risk of mortality is necessary for public health policy.

Forecasting Inequalities in Survival to Retirement Age by Socioeconomic Status in Denmark and Sweden.

In Denmark and Sweden, statutory retirement age is indexed to life expectancy to account for mortality improvements in their populations. However, mortality improvements have not been uniform across different sub-populations. Notably, in both countries, individuals of lower socioeconomic status (SES) have experienced slower mortality improvements. As a result, a uniform rise in the statutory retirement age could disproportionally affect these low-SES groups and may unintentionally lead to a reverse redistribution effect, shifting benefits from short-lived low-SES individuals to long-lived high-SES individuals. The aim of this study is twofold: to quantify and contextualise mortality inequalities by SES in Denmark and Sweden, and to assess how indexing retirement age will affect future survival to retirement age by SES in these countries. We used Danish and Swedish registry data (1988-2019), to aggregate individuals aged 50 + based on their demographic characteristics and SES. We computed period life tables by year, sex, and SES to estimate the difference in survival across different SES groups. We then forecast mortality across SES groups to assess how indexing retirement age will affect survival inequalities to retirement age, using two forecasting models-the Mode model and the Li-Lee model. Mortality inequalities are comparable in Denmark and Sweden, even though the latter generally has higher survival. We also find that indexing retirement age to life expectancy will have two main consequences: it will reduce the probability of reaching retirement for all SES groups, particularly those of low SES, and time spent in retirement will be reduced, particularly for those of high SES.

The impact of COVID-19 on life expectancy across socioeconomic groups in Denmark.

BACKGROUND: Denmark was one of the few countries that experienced an increase in life expectancy in 2020, and one of the few to see a decrease in 2021. Because COVID-19 mortality is associated with socioeconomic status (SES), we hypothesize that certain subgroups of the Danish population experienced changes in life expectancy in 2020 and 2021 that differed from the country overall. We aim to quantify life expectancy in Denmark in 2020 and 2021 by SES and compare this to recent trends in life expectancy (2014-2019). METHODS: We used Danish registry data from 2014 to 2021 for all individuals aged 30+. We classified the study population into SES groups using income quartiles and calculated life expectancy at age 30 by year, sex, and SES, and the differences in life expectancy from 2019 to 2020 and 2020 to 2021. We compared these changes to the average 1-year changes from 2014 to 2019 with 95% confidence intervals. Lastly, we decomposed these changes by age and cause of death distinguishing seven causes, including COVID-19, and a residual category. RESULTS: We observed a mortality gradient in life expectancy changes across SES groups in both pandemic years. Among women, those of higher SES experienced a larger increase in life expectancy in 2020 and a smaller decrease in 2021 compared to those of lower SES. Among men, those of higher SES experienced an increase in life expectancy in both 2020 and 2021, while those of lower SES experienced a decrease in 2021. The impact of COVID-19 mortality on changes in life expectancy in 2020 was counterbalanced by improvements in non-COVID-19 mortality, especially driven by cancer and cardiovascular mortality. However, in 2021, non-COVID-19 mortality contributed negatively even for causes as cardiovascular mortality that has generally a positive impact on life expectancy changes, resulting in declines for most SES groups. CONCLUSIONS: COVID-19 mortality disproportionally affected those of lower SES and exacerbated existing social inequalities in Denmark. We conclude that in health emergencies, particular attention should be paid to those who are least socially advantaged to avoid widening the already existing mortality gap with those of higher SES. This research contributes to the discussion on social inequalities in mortality in high-income countries.

Positive and negative syndrome scale in forensic patients with schizophrenia spectrum disorders: a systematic review and meta-analysis.

Among forensic patients with schizophrenia spectrum disorders, the association between symptomatology and violence is still not entirely clear in literature, especially because symptoms shift both during the acute phase of the illness and after. The aims were to investigate the level of symptomatology in forensic patients and to evaluate if there are differences in the level of symptoms between forensic and non-forensic patients. According to PRISMA guidelines, a systematic search was performed in PubMed, Web of Science, and ProQuest, using the following key words: "forensic" AND "Positive and Negative Syndrome Scale" OR "PANSS". A total of 27 studies were included in the systematic review, while only 23 studies in the meta-analysis. The overall sample included a total of 1702 participants, most commonly male and inpatients in forensic settings. We found that studies with an entirely male sample had significantly lower Positive PANSS ratings than studies with mixed samples. Although both forensic and non-forensic patients were affected by mild psychopathological symptoms, forensic patients presented higher ratings in all four PANSS scales. This meta-analysis shows that forensic patients reported a mild level of symptomatology, as assessed with the PANSS, and therefore might be considered as patients in partial remission. Among patients with schizophrenia, the association between symptoms and violence is very complex: many factors might be considered as key mediators and thus should be taken into account to explain this association. Further studies are needed.Trial registration all materials and data can be found on the OSF framework: https://osf.io/5ceja (date of registration: 8 September 2021).

Sex-differences in excess death risk during the COVID-19 pandemic: an analysis of the first wave across Italian regions. What have we learned?

In this commentary, we bring together knowledge on sex-differences in excess death during the first wave of the COVID-19 pandemic in Italy, one of the most hit European countries. We zoom into Italian regions to account for the spatial gradient of the spread of the virus. Analyses of excess death by sex during the COVID-19 pandemic have been possible thanks to weekly mortality data released by national statistical offices, mainly in developed countries. The general finding is that males up to 75 years old have been suffering more excess death compared to females. However, the picture is less clear-cut at older ages. During previous epidemics, such as SARS, Swine Flu, and MERS, studies are limited and produce scattered, non-conclusive evidence. Knowledge of the sex-pattern of susceptibility to mortality from virulent respiratory diseases and its interplay with age could improve crisis management during future epidemics and pandemics. National statistical offices should provide weekly mortality data with spatial granularity, disaggregated by sex and age groups, to allow for such analyses.

Stratification in health and survival after age 100: evidence from Danish centenarians.

BACKGROUND: The existence of a super-select group of centenarians that demonstrates increased survivorship has been hypothesized. However, it is unknown if this super-select group possesses similar characteristics apart from extreme longevity. METHODS: In this study, we analyse high-quality health and survival data of Danish centenarians born in 1895, 1905 and 1910. We use Latent Class Analysis to identify unobserved health classes and to test whether these super-select lives share similar health characteristics. RESULTS: We find that, even after age 100, a clear and distinct gradient in health exists and that this gradient is remarkably similar across different birth cohorts of centenarians. Based on the level of health, we identify three clusters of centenarians - robust, frail and intermediate - and show that these groups have different survival prospects. The most distinctive characteristic of the robust centenarians is the outperformance in different health dimensions (physical, functional and cognitive). Finally, we show that our health class categorizations are good predictors of the survival prospects of centenarians. CONCLUSIONS: There is a clear stratification in health and functioning among those over 100 years of age and these differences are associated with survival beyond age 100.

Regularities in human mortality after age 105.

Empirical research on human mortality and extreme longevity suggests that the risk of death among the oldest-old ceases to increase and levels off at age 110. The universality of this finding remains in dispute because of two main reasons: i) high uncertainty around statistical estimates generated from scarce data, and ii) the lack of country-specific comparisons. In this article, we estimate age patterns of mortality above age 105 using data from the International Database on Longevity, an exceptionally large and recently updated database comprising more than 13,000 validated records of long-lived individuals from eight populations. We show that, in all of them, similar mortality trajectories arise, suggesting that the risk of dying levels off after age 105. As more high-quality data become available, there is more evidence in support of a levelling-off of the risk of dying as a regularity of longevous populations.

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality.

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy).

BACKGROUND: Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles. METHODS: Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis. RESULTS: This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: "healthy", "physically healthy with cognitive impairment", "unhealthy", and "severely unhealthy". Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers. CONCLUSIONS: Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.

Understanding Health Deterioration and the Dynamic Relationship between Physical Ability and Cognition among a Cohort of Danish Nonagenarians.

This study aims to determine how demographics, socioeconomic characteristics, and lifestyle affect physical and cognitive health transitions among nonagenarians, whether these transitions follow the same patterns, and how each dimension affects the transitions of the other. We applied a multistate model for panel data to 2262 individuals over a 2-year follow-up period from the 1905 Danish Cohort survey. Within two years from baseline, the transition probability from good to bad physical health-ability to stand up from a chair-was higher than dying directly (29% vs. 25%), while this was not observed for cognition (24% vs. 27%) evaluated with Mini-Mental State Examination-a score lower than 24 indicates poor cognitive health. Probability of dying either from bad physical or cognitive health condition was 50%. Health transitions were associated with sex, education, living alone, body mass index, and physical activity. Physical and cognitive indicators were associated with deterioration of cognitive and physical status, respectively, and with survivorship from a bad health condition. We conclude that physical and cognitive health deteriorated differently among nonagenarians, even if they were related to similar sociodemographic and lifestyle characteristics and resulted dynamically related with each other.