Trends in cancer-free working life expectancy based on health insurance data from Germany-Is the increase as strong as in working life expectancy?

BACKGROUND: Against the backdrop of population ageing, governments are facing the need to raise the statutory retirement age. In this context, the question arises whether these extra years added to working life would be spent in good health. As cancer represents a main contributor to premature retirement this study focuses on time trends and educational inequalities in cancer-free working life expectancy (WLE). METHODS: The analyses are based on the data of a large German health insurer covering annually about 2 million individuals. Cancer-free WLE is calculated based on multistate life tables and reported for three periods: 2006-2008, 2011-2013, and 2016-2018. Educational inequalities in 2011-2013 were assessed by two educational levels (8 to 11 years and 12 to 13 years of schooling). RESULTS: While labour force participation increased, cancer incidence rates decreased over time. Cancer-free WLE at age 18 increased by 2.5 years in men and 6.3 years in women (age 50: 1.3 years in men, 2.4 years in women) between the first and third period while increases in WLE after a cancer diagnosis remained limited. Furthermore, educational inequalities are substantial, with lower groups having lower cancer-free WLE. The proportion of cancer-free WLE in total WLE remained constant in women and younger men, while it decreased in men at higher working age. CONCLUSION: The increase in WLE is accompanied by an increase in cancer-free WLE. However, the subgroups considered have not benefitted equally from this positive development. Among men at higher working age, WLE increased at a faster pace than cancer-free WLE. Particular attention should be paid to individuals with lower education and older men, as the general level and time trends in cancer-free WLE are less favourable.

A global assessment of the impact of violence on lifetime uncertainty.

Uncertainty around age at death, or lifetime uncertainty, is a key public health indicator and a marker of inequality in survival. How does the extent of violence affect lifetime uncertainty? We address this question by quantifying the impact of violence on dispersion in the ages at death, the metric most used to measure lifetime uncertainty. Using mortality data from the Global Burden of Disease Study and the Internal Peace Index between 2008 and 2017, we find that the most violent countries are also those with the highest lifetime uncertainty. In the Middle East, conflict-related deaths are the largest contributor to lifetime uncertainty. In Latin America, a similar pattern is attributable to homicides. The effects are larger in magnitude for men, but the consequences remain considerable for women. Our study points to a double burden of violence on longevity: Not only does violence shorten individual lives, but it also makes the length of life less predictable.

Socioeconomic environment and survival in patients after ST-segment elevation myocardial infarction (STEMI): a longitudinal study for the City of Vienna.

OBJECTIVES: This study investigates the relationship between socioeconomic environment (SEE) and survival after ST-segment elevation myocardial infarction (STEMI) separately for women and men in the City of Vienna, Austria. DESIGN: Hospital-based observational data of STEMI patients are linked with district-level information on SEE and the mortality register, enabling survival analyses with a 19-year follow-up (2000-2018). SETTING: The analysis is set at the main tertiary care hospital of the City of Vienna. On weekends, it is the only hospital in charge of treating STEMIs and thus provides representative data for the Viennese population. PARTICIPANTS: The study comprises a total of 1481 patients with STEMI, including women and men aged 24-94 years. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures are age at STEMI and age at death. We further distinguish between deaths from coronary artery disease (CAD), deaths from acute coronary syndrome (ACS), and other causes of death. SEE is proxied via mean individual gross income from employment in each municipal district. RESULTS: Results are based on Kaplan-Meier survival probability estimates, Cox proportional hazard regressions and competing risk models, always using age as the time scale. Descriptive findings suggest a socioeconomic gradient in the age at death after STEMI. This finding is, however, not supported by the regression results. Female patients with STEMI have better survival outcomes, but only for deaths related to CAD (HR: 0.668, 95% CIs 0.452 to 0.985) and other causes of deaths (HR: 0.627, 95% CIs 0.444 to 0.884), and not for deaths from the more acute ACS. CONCLUSIONS: Additional research is necessary to further disentangle the interaction between SEE and age at STEMI, as our findings suggest that individuals from poorer districts have STEMI at younger ages, which indicates vulnerability in regard to health conditions in these neighbourhoods.

The impact of COVID-19 vaccines on the Case Fatality Rate: The importance of monitoring breakthrough infections.

OBJECTIVES: This study aimed to test the behavior of the case fatality rate (CFR) in a mixed population of vaccinated and unvaccinated individuals by illustrating the role of both the effectiveness of vaccines in preventing deaths and the detection of infections among both the vaccinated (breakthrough infections) and unvaccinated individuals. METHODS: We simulated three hypothetical CFR scenarios that resulted from a different combination of vaccine effectiveness in preventing deaths and the efforts in detecting infections among both the vaccinated and unvaccinated individuals. RESULTS: In the presence of vaccines, the CFR depends not only on the effectiveness of vaccines in preventing deaths but also on the detection of breakthrough infections. As a result, a decline in the CFR may not imply that vaccines are effective in reducing deaths. Likewise, a constant CFR can still mean that vaccines are effective in reducing deaths. CONCLUSIONS: Unless vaccinated people are also tested for COVID-19 infection, the CFR loses its meaning in tracking the pandemic. This shows that unless efforts are directed at detecting breakthrough infections, it is hard to disentangle the effect of vaccines in reducing deaths from the probability of detecting infections on the CFR.

An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39-2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21-1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

Life Expectancy: Frequently Used, but Hardly Understood.

Period life expectancy is one of the most used summary indicators for the overall health of a population. Its levels and trends direct health policies, and researchers try to identify the determining risk factors to assess and forecast future developments. The use of period life expectancy is often based on the assumption that it directly reflects the mortality conditions of a certain year. Accordingly, the explanation for changes in life expectancy are typically sought in factors that have an immediate impact on current mortality conditions. It is frequently overlooked, however, that this indicator can also be affected by at least three kinds of effects, in particular in the situation of short-term fluctuations: cohort effects, heterogeneity effects, and tempo effects. We demonstrate their possible impact with the example of the almost Europe-wide decrease in life expectancy in 2015, which caused a series of reports about an upsurge of a health crisis, and we show that the consideration of these effects can lead to different conclusions. Therefore, we want to raise an awareness concerning the sensitivity of life expectancy to sudden changes and the menaces a misled interpretation of this indicator can cause.