Retirement timing in Italy: rising age and the advantages of a stable working career

Mortality Among Dental Healthcare Workers During the Coronavirus Disease 2019 Pandemic: A Public Domain Database Study

BACKGROUND

This study was conducted to assess the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on dental healthcare workers (DHCWs) during the pandemic. Due to frequent exposure to aerosol-generating procedures, DHCWs are at an increased risk of a SARS-CoV-2 infection, increasing their mortality risk. Therefore, this retrospective study aimed to ascertain the mortality of DHCWs attributable to SARS-CoV-2 infection from databases in the public domain during the pandemic.

METHODS

The data were obtained from three public-domain online databases: Medscape, FNOMCeO, and X. All collected data from March 2020 to May 2024 were merged into a tabulated format. An analysis was conducted on the data collectively and through a subgroup analysis based on the country, detailed profession, age, gender, and date of death.

RESULTS

During the SARS-CoV-2 pandemic, 100 DHCWs were deceased. The DHCWs in the study were employed in 14 countries, with the United States (40%) and Italy (34%) listing the highest percentages of deaths of these workers. Dentists constituted 79% of the total DHCWs. The gender distribution among the deceased was 85 (85%) men and 15 (15%) women. The age of 57 deceased DHCWs was known, resulting in a mean age of 58 years (ranging from 28 to 88). The DHCWs at ages 60 to 70 years (49%) exhibited the highest mortality rate from SARS-CoV-2. The date of death was known for 73 DHCWs, who were deceased between March 2020 and February 2023.

CONCLUSION AND CLINICAL RELEVANCE

During the global pandemic, DHCWs worked in the context of general practice. The DHCWs adhered to infection prevention protocols according to standard guidelines and incorporated new adjunctive measures, especially for the pandemic, including adopting coronavirus disease 2019 guidelines for oral healthcare. These measures provided satisfactory protection, with less than 0.006% mortality of the estimated DHCWs worldwide.

Increasing retirement ages in Denmark: Do changes in gender, education, employment status and health matter?

Recent studies report significant increases in retirement ages over the past two to three decades in most countries in the Organization for Economic Co-operation and Development-increases that research has attributed mainly to changes in the legislative frameworks for retirement in these countries. Using unique data from the Danish Longitudinal Study of Ageing, this study investigates whether and, if so to what extent, changes to the workforce in terms of gender, education, employment status (employed or self-employed) and health contribute to explaining differences in retirement ages between the cohorts born in 1935 and 1950. The retirement window of these cohorts stretches from the early 1990s to the late 2010s-a period characterized by substantial changes to workforce. On average, retirement ages increased by two years from the 1935 cohort to the 1950 cohort. However, due to changes in the investigated factors having offsetting effects, the net effect of such changes on retirement ages was minor. Thus, while increasing levels of education and better health among older workers contributed to increasing retirement ages, increasing female labour force participation and fewer self-employed workers had the opposite effect. In absolute terms, the total compositional and behavioural influence on retirement ages of changes in terms of employment status (- 0.35 years) was almost as large as the total changes in terms of education (0.44 years). Thus, future studies investigating long-term changes in retirement ages would benefit from including changes in employment status (self-employed or wage earner) as an explanatory factor.

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.