“Intelligence and Life Expectancy in Late Adulthood: A Meta-Analysis”, Macarena Sánchez-Izquierdo, Rocío Fernández-Ballesteros, Elizabeth Lucía Valeriano-Lorenzo, Juan Botella2023-05 (, )⁠:

In an aging society, it is crucial to understand why some people live long and others do not. There has been a proliferation of studies in recent years that highlight the importance of psycho-behavioral factors in the ways of aging, one of those psychological components is intelligence.

In this meta-analysis, the association between intelligence and life expectancy in late adulthood is analysed through the hazard ratio (HR). Our objectives are: (1) to update Calvin et al 2011’s meta-analysis, especially the estimate of the association between survival and intelligence; and (2) to evaluate the role of some moderators, especially the age of the participants, to explore intelligence-mortality throughout adulthood and old age.

The results show a positive relationship between intelligence and survival (HR: 0.79; 95% CI: 0.81–0.76). This association is statistically-significantly moderated by the years of follow-up, the effect size being smaller the more years elapse between the intelligence assessment and the recording of the outcome.

Intelligence is a protective factor to reach middle-high age, but from then on survival depends less and less on intelligence and more on other factors.

[Keywords: intelligence, mortality, meta-analysis, systematic review]

…Having an intelligence of at least 1-SD above the mean reduces the mortality rate by about 21.6%. The 12 independent estimates that controlled for childhood SES revealed a similar pooled effect size [HR = 0.788; 95% CI: 0.759–0.817], according to which high intelligence seems to reduce mortality by about 21.2%. Childhood SES does not moderate the potential of intelligence for predicting mortality.

Figure 2: Forest Plot with the 25 separate estimates of ‘intelligence’. Intelligence is defined as being (versus not being) above the mean in at least one standard deviation (See description in Method).

…3.4. Publication bias: As publication bias can give rise to overestimates of the effect size, we evaluated the degree to which this anomaly could be a potential threat to the results of this meta-analysis. Egger’s test (Test = −20.73; p = 0.04) revealed a statistically-significant asymmetry that was clearly visible in the funnel plot (Figure 3), whereas the rank correlation test did not reach statistical-significance (Test = −0.15; p = 0.32).

When applying the Trim and Fill method, 4 missing estimates were imputed. Of course, the estimated association after the imputation was smaller [HR = 0.79; 95% CI: 0.77–0.83], but close to the uncorrected estimation…In summary, we believe that the observed effect is not the product of massive publication bias and that the possible effect of overestimating the pooled ES is small and does not change the conclusions of the meta-analysis.