Retirement age does not modify the association of prior working conditions with self-rated health and mortality in retirees: results from a prospective study of retired French workers.

OBJECTIVE: It is unclear whether retirement age can modify the association of working conditions with health and mortality in retirees who are no longer exposed to these conditions. METHODS: The present study investigated this issue in a cohort of 13,378 French workers in whom self-rated health and mortality were measured over 15 years after statutory retirement. The analyses were also performed in homogenous clusters of workers differentiated on the basis of working conditions, social position, birth and retirement years. RESULTS: Bad working conditions before retirement, which were assessed using a global score combining 25 different occupational exposures, were associated with higher rates of suboptimum self-rated health and mortality in retirees after adjusting for retirement age, social position, demographics and health status before retirement. These rates were also substantially higher in the cluster of workers characterized by bad working conditions in comparison to other clusters. In contrast, retirement age was not associated with self-rated health or mortality after adjusting for working conditions, social position, demographics and health status before retirement. Likewise, no association of retirement age with self-rated health or mortality was found in any cluster of workers and no interactions were observed with any of these clusters. CONCLUSION: These results suggest that bad working conditions before retirement have long-term detrimental effects on health and mortality in retirees and that retirement age does not modulate these effects. Improving work environment rather than modifying retirement age should be prioritized to promote health and reduce mortality not only in workers but also in retirees.

Author Correction: A stochastic agent-based model of the SARS-CoV-2 epidemic in France.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A stochastic agent-based model of the SARS-CoV-2 epidemic in France.

Many European countries have responded to the COVID-19 pandemic by implementing nationwide protection measures and lockdowns1. However, the epidemic could rebound when such measures are relaxed, possibly leading to a requirement for a second or more, repeated lockdowns2. Here, we present results of a stochastic agent-based microsimulation model of the COVID-19 epidemic in France. We examined the potential impact of post-lockdown measures, including physical distancing, mask-wearing and shielding individuals who are the most vulnerable to severe COVID-19 infection, on cumulative disease incidence and mortality, and on intensive care unit (ICU)-bed occupancy. While lockdown is effective in containing the viral spread, once lifted, regardless of duration, it would be unlikely to prevent a rebound. Both physical distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing ICUs from becoming overwhelmed and a subsequent second lockdown. However, these measures coupled with the shielding of vulnerable people would be associated with better outcomes, including lower mortality and maintaining an adequate ICU capacity to prevent a second lockdown. Benefits would nonetheless be markedly reduced if most people do not adhere to these measures, or if they are not maintained for a sufficiently long period.

Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France.

Most European countries have responded to the COVID-19 threat by nationwide implementation of barrier measures and lockdown. However, assuming that population immunity will build up through the epidemic, it is likely to rebound once these measures are relaxed, possibly leading to a second or multiple repeated lockdowns. In this report, we present results of epidemiological modelling that has helped inform policy making in France. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in France, and examined the potential impact of post-quarantine measures, including social distancing, mask-wearing, and shielding of the population the most vulnerable to severe COVID-19 infection, on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is effective in containing the viral spread, it would be unlikely to prevent a rebound of the epidemic once lifted, regardless of its duration. Both social distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing the overwhelming of ICUs and a second lockdown. However, these measures coupled with shielding of vulnerable people would be associated with better outcomes, including lower cumulative incidence, mortality, and maintaining an adequate number of ICU beds to prevent a second lockdown. Benefits would nonetheless be markedly reduced if these measures were not applied by most people or not maintained for a sufficiently long period, as herd immunity progressively establishes in the less vulnerable population.