Association between retirement and mortality: working longer, living longer? A systematic review and meta-analysis. Issue 5 (20th February 2020) - Sewdas, Ranu; de Wind, Astrid; Stenholm, Sari; Coenen, Pieter; Louwerse, Ilse; Boot, Cécile; van der Beek, Allard
Abstract : Aim: This study summarised available evidence on the association between early and on-time retirement, compared with continued working, and mortality. Moreover, this study investigated whether and to what extent gender, adjustment for demographics and prior health status influence this association. Methods: A systematic literature search of longitudinal studies was conducted. A qualitative analysis of the included studies was performed, followed by a meta-regression analysis to assess the influence of gender, prior health and demographics. Random-effects models were used in a meta-analysis to estimate the pooled effects for relevant subgroups identified in the meta-regression. Results: In total, 25 studies were included. Adjustment for prior health and demographics influenced the association between retirement and mortality (p<0.05). The results of the meta-analysis of 12 studies are presented for 'insufficiently adjusted' and 'fully adjusted' subgroups. There was no association between early retirement and mortality compared with working until retirement (fully adjusted subgroup: HR 1.05, 95% CI 0.87 to 1.28). On-time retirement was associated with a higher risk of mortality compared with working beyond retirement (insufficiently adjusted subgroup: HR 1.56, 95% CI 1.41 to 1.73). However, in the subgroup that adjusted for prior health, on-time retirement was not associated with mortality (HR 1.12, 95% CI 0.98 to 1.28). Conclusion: Early retirement was not associated with a higher risk of mortality. On-time retirement was associated with a higher risk of mortality, which might reflect the healthy worker effect. It is important to consider information on prior health and demographics when studying the association between retirement and mortality to avoid biased findings.