The life expectancies of men and women are widely recognized as being different: women worldwide live longer than men1. This logically leads to the question whether women also age slower than men. Both “yes” and “no” answers have found some support2,3,4. The classical argument against the notion that women age slower is the fact that men experience higher mortality rates at almost every age, i.e. that the reason for their shorter lifespan is that men are the less “robust” sex and as such exhibit higher background mortality2,4. On the other hand, researchers suggesting that women age slower than men note that this line of reasoning may not be altogether valid since men die from different causes at different ages3. Regardless of theoretical arguments, aging can be defined as an age-dependent increase in mortality3,5,6 and the pace of aging of men and women may therefore be empirically calculated using available mortality data an approach we employed in this article.

In addition to above-mentioned research from the field of gerontology, the longer lifespan of women has been a focus of demographic research since the mid-18th century7. Various explanations have been suggested, including biological factors, risk acquired through the social roles, environmental conditions and behavior8,9. And while the search for a precise answer to the question „what is the cause of a longer female lifespan?” continues, it seems that a great portion of female lifespan advantage is caused by lifestyle choices. This is supported by a data showing that societies in which lifestyles of men and women are more homogenous than in general population have much smaller lifespan difference between sexes, examples of such populations are catholic religious orders9, kibbutzes10, Seventh-Day Adventists11, and others12,13,14. Accordingly, men have higher variability in mortality than women7. Nevertheless, even in the case of e.g. Catholic religious orders women still live longer than men9.

One method of quantifying aging relies on calculating the rate at which mortality increases with age15. The relationship between human age and mortality is usually modeled using a predefined distribution which explicitly defines the relationship between age and mortality rate. Distributions most commonly used for this purpose include Gompertz, its extension Gompertz–Makeham, Weibull or logistic16. The choice of a specific distribution depends on the purpose of its use: the best-fitting model is often desired when a prediction is sought while a different model may be more suitable for the interpretation of parameter values17,18. Since our objective was to test the difference between the pace of mortality rate increase in men and women, the Gompertz model19 was selected as a simple and suitable option. In addition to accommodating human mortality data between approximately 30 and 80 years of age18,20, it also offers a means for comparing mortality rate increase by means of mortality rate doubling time (MRDT), a parameter commonly used as an estimate of the rate of aging21,22. On the other hand, it does not distinguish between intrinsic and extrinsic mortality rates, where intrinsic mortality is assumed to be the result of aging and increases over time while extrinsic mortality is assumed to be caused by environmental hazards and is thus constant over time23. This inability to distinguish between intrinsic and extrinsic mortality rates is to some extent alleviated by the fact that mortality within the chosen interval of 30 to 60 years of age is mainly influenced by intrinsic causes18. However, even though the extrinsic causes are responsible for a minority of deaths within the chosen interval, they still affect the overall mortality rates. The Gompertz–Makeham model extends the Gompertz model to include mortality rate independent of age. This partitioning of mortality rates into an age-related and a constant component is clearly helpful when analyzing the rates of aging.

In this study we used mortality data obtained from the Human Mortality Database24 to calculate MRDTs using the Gompertz and Gompertz–Makeham model for male and female populations in 13 high-income countries. Furthermore, we have also employed two non-parametric approaches to compare the pace of aging between sexes. However, it must be said that mortality rates are affected by a great variety of external influences unrelated to aging. One extreme example of such external influences was undoubtedly World War II, which dramatically altered mortality rates both directly through the deaths of millions of soldiers and civilians and indirectly through the late effects of injuries, starvation, psychological trauma, etc. It is known that mortality rates during the early life of a cohort influence its mortality rates later in life25 which makes cohorts affected by a WWII unsuitable for comparing the pace of aging between sexes. Because most countries in the Human Mortality Database were more or less heavily involved in WWII, we analyzed mortality patterns only in people born at least five years after the end of this conflict. To be more specific, we analysed cohorts of people born from 1950 to 1954 using cohort mortality rates in periods starting from 1980 to 1984 to the newest available data in the Human Mortality Database. In other words, investigated mortality rates were calculated using periods starting with the subjects’ 30th birthdays and ending with the end of records.