Women live longer than men even during severe famines and epidemics

Abstract

Significance
Women live longer than men in nearly all populations today. Some research focuses on the biological origins of the female advantage; other research stresses the significance of social factors. We studied male–female survival differences in populations of slaves and populations exposed to severe famines and epidemics. We find that even when mortality was very high, women lived longer on average than men. Most of the female advantage was due to differences in mortality among infants: baby girls were able to survive harsh conditions better than baby boys. These results support the view that the female survival advantage is modulated by a complex interaction of biological environmental and social factors.

Full text

Women live longer than men even during severe
famines and epidemics
Virginia Zarulli
a,b
, Julia A. Barthold Jones
a,b
, Anna Oksuzyan
c
, Rune Lindahl-Jacobsen
a,b
, Kaare Christensen
a,b,d,e
,
and James W. Vaupel
a,b,f,g,1
a
Max Planck Odense Center on the Biodemography of Aging, University of Southern Denmark, DK-5230 Odense, Denmark;
b
Department of Public Health,
University of Southern Denmark, DK-5000 Odense, Denmark;
c
Max Planck Research Group Gender Gaps in Health and Survival, Max Planck Institute for
Demographic Research, 18057 Rostock, Germany;
d
Department of Clinical Genetics, Odense University Hospital, DK-5000 Odense, Denmark;
e
Department of
Clinical Biochemistry and Pharmacology, Odense University Hospital, DK-5000 Odense, Denmark;
f
Max Planck Institute for Demographic Research, 18057
Rostock, Germany; and
g
Duke University Population Research Institute, Duke University, Durham, NC 27708
Contributed by James W. Vaupel, November 22, 2017 (sent for review February 6, 2017; reviewed by Tommy Bengtsson and France Mesle)
Women in almost all modern populations live longer than men.
Research to date provides evidence for both biological and social
factors influencing this gender gap. Conditions when both men
and women experience extremely high levels of mortality risk are
unexplored sources of information. We investigate the survival of
both sexes in seven populations under extreme conditions from
famines, epidemics, and slavery. Women survived better than
men: In all populations, they had lower mortality across almost all
ages, and, with the exception of one slave population, they lived
longer on average than men. Gender differences in infant mortal-
ity contributed the most to the gender gap in life expectancy,
indicating that newborn girls were able to survive extreme
mortality hazards better than newborn boys. Our results confirm
the ubiquity of a female survival advantage even when mortality
is extraordinarily high. The hypothesis that the survival advantage
of women has fundamental biological underpinnings is supported
by the fact that under very harsh conditions females survive better
than males even at infant ages when behavioral and social differ-
ences may be minimal or favor males. Our findings also indicate
that the female advantage differs across environments and is
modulated by social factors.
famines
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epidemics
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mortality
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survival
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gender
Women are the life-expectancy champions: They can expect
to live longer than men almost anywhere in the world
today (1–3). This pervasive inequality has intrigued researchers
for decades (4). The cumulative corpus of research supports the
conclusion that the gap has biological underpinnings modulated
by social and environmental conditions. Deeper understanding
could benefit from biodemographic research (5). Here we pre-
sent some results of such research.
Support for a biological root of the gender gap in survival
stems from studies of groups in which men and women have
more similar lifestyles than in the general population, such as
among nonsmokers (6, 7) or within religious groups such as ac-
tive Mormons (8) or cloistered monks and nuns (9). Findings
indicate that, even though men and women in these groups have
more similar lifestyles and men are exposed to fewer risk factors
than men in the general population, a gender gap in life expec-
tancy still persists. Excess male mortality is also found among
newborns and infants (10–12), when behavioral differences are
unlikely to play a crucial role and social factors may be neutral or
favor male survival. An untapped source of information is the
reverse situation, when both men and women experience high,
perhaps extreme, levels of mortality risk. A finding that men and
women have similar life expectancies under these conditions
would challenge the notion that the survival advantage of women
is fundamentally biologically determined in all environments.
Therefore, we study here the survival of both sexes in pop-
ulations enduring mortality crises.
While women have lower mortality than men in modern pop-
ulations, evidence for a female survival advantage under crisis
conditions is sparse. A well-known story concerns the Donner
Party, a group of settlers that lost twice as many men as women
when stranded for 6 mo in the extreme winter in the Sierra
Nevada mountains (13). While accounts like this are anecdotal, a
variety of studies provide evidence that women appear to survive
cardiovascular diseases, cancers, and disabilities longer than men
(14–18). However, the generality of this notion needs to be treated
with caution, since findings on sex differences in survival after
myocardial infarction and stroke are mixed (19–21).
Additional support for female hardiness comes from the fact
that, in most countries, the sex difference in remaining healthy
life expectancy is smaller than the difference in total life expectancy.
The difference becomes even smaller later in life: For example, the
gender gap in life expectancy at age 65 y for France and Sweden in
2015 was 4.1 and 2.6 y, respectively, while the gap in healthy life
expectancy at age 65 y was only 0.9 and 1.1 y (ec.europa.eu/eurostat/
web/population-demography-migration-projections/deaths-life-
expectancy-data/main-tables;ec.europa.eu/eurostat/statistics-explained/
images/3/3d/Healthy_life_years%2C_2015_%28years%29_YB17.png).
Thus women live more years than men and are able to do so even
though they are in bad health for a substantial part of those extra
years of life.
We investigate whether the ability of women to survive better
under difficult circumstances extends to crises such as famines,
epidemics, or slavery.
Significance
Women live longer than men in nearly all populations today.
Some research focuses on the biological origins of the female
advantage; other research stresses the significance of social
factors. We studied male–female survival differences in pop-
ulations of slaves and populations exposed to severe famines
and epidemics. We find that even when mortality was very
high, women lived longer on average than men. Most of the
female advantage was due to differences in mortality among
infants: baby girls were able to survive harsh conditions better
than baby boys. These results support the view that the female
survival advantage is modulated by a complex interaction of
biological environmental and social factors.
Author contributions: V.Z., R.L.-J., and J.W.V. designed research; V.Z. performed research;
V.Z. analyzed data; J.A.B.J. interpreted and discussed the results from an evolutionary
biology perspective; and A.O. contributed to the literature review and the discussion
about biological explanations for the women’s survival advantage (based on human
studies); and V.Z., J.A.B.J., A.O., K.C., and J.W.V. wrote the paper.
Reviewers: T.B., Lund University; and F.M., Institut National d’Études Démographiques.
The authors declare no conflict of interest.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives L icense 4. 0 (CC BY-N C-ND).
1
To whom correspondence should be addressed. Email: jwv@demogr.mpg.de.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1701535115/-/DCSupplemental.
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Data and Methods
High-Mortality Populations. We analyzed seven documented populations with
extremely low life expectancies (20 y or less) for at least one of the sexes, due
to extreme conditions such as famines, epidemics, or slavery. Even though a
life expectancy of 20 y might seem unrealistically low, some populations in
temporarily extreme conditions had a life expectancy below this value (22).
Historical demographic data can be often problematic. However, the cases
used in this study have all been previously published in a respected peer-
reviewed journal. This, by itself, should ensure data quality and reliability.
Nevertheless, for each case we discuss potential problems and biases that
can affect the gender difference in survival.
Freed Liberian slaves (23, 24). Between 1820 and 1843, freed American slaves
were encouraged to migrate back to Africa. Many undertook the risky trip
and went to Liberia, where they encountered a very different disease en-
vironment than the one in which they grew up. McDaniel (24) used data
collected by the American Colonization Society from 1820 to 1843 and es-
timated life tables for the former slaves. The data show the highest mor-
tality ever registered in recorded human history. The arrival in Liberia was a
mortality shock. About 43% died during the first year, and life expectancy at
birth was 1.68 y for men and 2.23 y for women. Data come from a dataset
referred to as “the roll,”which lists those who emigrated from the United
States under the monitoring of the American Colonization Society and the
US government between 1820 and 1843. McDaniel and Preston (23) and
McDaniel (24) performed multiple data checks, matching them with other
administrative sources, and concluded that the emigrant population was
monitored carefully. The data were then compared with the model life ta-
bles and show the same age pattern as the so-called “North model”but at
more extreme levels. That is, the data show a pattern of mortality over age
that corresponds with that of other human populations. However, even
though the determination of gender of the individuals (which was not
available in the original data) was carefully done with special software and a
name dictionary, following a rigorous procedure, the sex of 181 of the
4,472 cases (4%) remained unidentified. This might have caused some bias in
the sex-specific death rates, but the percentage is small enough to conclude
that any bias would be weak.
Plantations slaves in Trinidad (25, 26). At the beginning of the 19th century pro-
and antislavery forces clashed about the emancipation of the slaves in the
British Caribbean. The antislavery campaign obtained an annual registration
of the slaves in the colony of Trinidad, the only colony controlled directly from
London. Since unregistered slaves were confiscated by the Crown, owners
had a strong incentive to comply with the order. The register contains the age
of slaves in 1813 and in 1816 and records how many deaths and births oc-
curred during the period. John (25) analyzed the data in the register. She
concluded the data were complete for all age groups except for infants (age
category 0–1 y), who were underreported, and that the data were affected
by age heaping on multiples of five. Unfortunately, this could not be
smoothed because the age and sex composition of the population was
shaped by the slave trade, but she limited its interference by estimating life
tables by 5-y age groups. She then produced period life tables for the male
and female slaves in the plantations of Trinidad, which showed that life
expectancy could have been as low as 15.18 y and 13.21 y (lower bound) or
19.45 y and 20.58 y (upper bound) for males and females, respectively. Most
of the variation derived from uncertainty about the level of infant mortality,
and this uncertainty could be a source of bias in the computation of the
gender survival gap. However, the author notes that most of this uncertainty
can be ruled out by conditioning the survival curves to having survived up to
age 1 y and that the conditional survival functions for the upper and lower
bounds of the life table are virtually identical (25). The conditional survival
curves, obtained by dividing each value of the survival curves by the survival
value at age 1 y (as reported in ref. 26), are very similar in their upper and
lower bounds. Most importantly, they show the same age and gender pat-
tern as the unconditional survival curves.
The Ukrainian famine in 1933 (27). In the twentieth century, the Ukraine ex-
perienced particularly turbulent demographic trends that mirror a history of
major crises. Among these, the great famine in 1933 that followed the
collectivization of agriculture is documented by Meslé and Vallin (27), who
painstakingly reconstructed several data series. They estimated that period
life expectancy during the crisis dropped to 7.3 y for men and 10.9 y for
women from 41.58 and 45.93 y for men and women, respectively (average of
the 5 y before). The authors used census data and vital registrations avail-
able before and after the crisis (in 1926 and 1939), between which they
applied a series of methods such as interpolation, forward and backward
projections, assumptions for fertility and migration during the period, and
correction coefficients for the underreported deaths during the crisis. The
basic idea was to compare the actual 1939 population reported in the census
with a hypothetical population that would have existed without the crisis, to
obtain the effect of the crisis corrected for fertility loss and migration flows
produced by the famine. These could be estimated only by applying some
assumptions. If the assumption of constant total fertility rate at the level of
1931 is not likely to affect the estimates by gender, the assumption related
to migration could introduce a bias in the estimated gender patterns of
survival, e.g., if, a sex was attributed with a different migration rate than the
actual one. However, the authors based their work upon a solid base of
historical and statistical references that represents the most reliable source
of available knowledge about that period.
The Swedish famine in 1772–1773 (28). This is described as the last major famine
that caused starvation across most of Sweden. Abnormal weather conditions
in the summer of 1771, followed by widespread crop failures, caused a sudden
and sharp increase in food prices. Consequently, mortality due to starvation
increased. When the difficult crop conditions continued throughout 1772,
mortality increased even further in 1773. Approximately 50% of the excess
mortality was due to dysentery, a disease related to the malnutrition (28).
Since the famine affected most of the Swedish population, we used male
and female life tables from the Human Mortality Database (www.mortality.
org) for Sweden in 1773, when life expectancy plummeted to 17.15 y for
males and 18.79 y for females. The Human Mortality Database is the best
source of historical and current death rates for national populations. The
very high quality of its data is ensured by the database being limited to
populations for which death registration and census data are virtually
complete, since this type of information is necessary for the uniform method
used to reconstruct the data series. Among the countries included in the
dataset, Sweden has the longest time series. The vital registration system in
this country was established in the 17th century and was serviceably accurate
by the mid-18th century.
The Icelandic epidemics in 1846 and 1882 (29). Because the population of Iceland
was small, measles was not endemic and was devastating when epidemics
struck. In 1846 and 1882 Iceland experienced its two major measles epidemics
of the 19th century. The disease spread rapidly through most of southern
Iceland, the most populated area of the country. Even though official reg-
istration of deaths by measles started only in 1904, the two epidemics were
documented in parish registries and reports from physicians. Both epidemics
spread from Danish boats landing in the late spring of the respective year.
Severe weather and unsanitary wet conditions facilitated the spread of the
disease by causing many complications such as diarrhea and chronic bronchitis
(29). We used life tables from the Human Mortality Database that show a
sudden drop in life expectancy of both sexes in 1846 (from 35.35 y to 17.86 y
for males and from 40.81 y to 18.82 y for females) and 1882 (from 37.62 y to
16.76 y for males and from 43.99 y to 18.83 y for females).
The Irish famine in 1845–1849 (30). By 1845 potatoes were the staple food for
the majority of the Irish. When the mold Phytophthora infestans infected the
plants and caused nearly total crop failures over three consecutive years, the
Irish population starved. The population shrank due to extremely high
mortality, emigration, and fewer births. Life tables for the famine years
were constructed by combining various data sources (30). Life expectancy
dropped from about 38 y for both sexes in the prefamine years to 18.7 y for
men and 22.4 y for women. The history of the Irish population was shaped
by extensive migration during both nonfamine and famine years. The two
major destinations were North America and Britain. Controlling for migra-
tion, therefore, became a crucial aspect of the reconstruction of the toll of
the famine, to reduce the impact of a potentially severe bias. Fortunately,
the authors had available several analyses of the migration flows, mostly
based on ship passenger lists and on British censuses (which recorded the
Irish-born population resident in Britain), which allowed them to estimate
quite precisely the age and sex profile of the migrants (for example, the
male:female ratio was about 60:40 for emigrants to North America and
55:45 for emigrants to Britain).
Mortality Comparison. We compared mortality between the sexes and among
the populations using different mortality measures, including the probability
of survival from birth to age x(henceforth “survival”), the probability of
dying between age xand x+1 (henceforth “mortality”), life expectancy (at
birth), and the age at which 5% of a synthetic same-sex cohort would still be
alive (henceforth “extreme age”). We took these measures from the life
tables, when available, and otherwise applied standard demographic
methods to compute the life tables (31). For comparing mortality between
the sexes, we further computed male:female mortality ratios and differences
for each population and decomposed the sex differences in life expectancy
by age (32).
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Results
Overview of Gender Difference in Mortality in Normal Conditions.
Data for pre- and postcrisis times were available only for the
Ukraine in 1933, Sweden in 1773, Iceland in 1846, and Iceland
in 1882. In these cases, during normal years, women had higher
life expectancy than men (Table 1). The female–male differ-
ence for each population was remarkably stable between the
precrisis and the postcrisis years (Table 1), a sign that the ep-
idemics or the famines affected the gender difference in sur-
vival only temporarily.
For the other populations analyzed in this study only partial
information about mortality in normal conditions was available.
For the freed Liberian slaves, a life table for those who survived
the critical first year of arrival shows that women had 1.25 y
longer life expectancy at age 1 y (the table necessarily excludes
the first year of life) than men: 24.62 y vs. 22.87 y. The estimated
Table 1. Absolute and relative differences in male and female life expectancy for seven high-mortality populations
during (and, when available, before and after) extreme mortality conditions
Population
Life expectancy Female–male difference in life expectancy
Male Female Absolute, y Relative
Pre Crisis Post Pre Crisis Post Pre Crisis Post Pre Crisis Post
Liberia 1820–1843 —1.68 22.87* —2.23 24.62* —0.55 1.25* —0.33 0.05*
Trinidad 1813–1816 —15.18–19.45
†
——13.21–20.58
†
——−1.27–1.13
†
——−0.08–0.06
†
—
Ukraine 1933 41.58 7.30 45.12 45.93 10.85 50.49 4.35 3.55 5.37 0.1 0.49 0.12
Sweden 1773 32.31 17.15 37.61 35.19 18.79 39.85 2.88 1.64 2.24 0.09 0.09 0.06
Iceland 1846 35.35 17.86 33.13 40.81 18.82 38.31 5.46 0.96 5.18 0.15 0.05 0.16
Iceland 1882 37.62 16.76 37.82 43.99 18.83 43.74 6.37 2.07 5.92 0.17 0.12 0.16
Ireland 1845–1849 38.3 18.7 —38.3 22.4 —0 3.70 —0 0.20 —
*Life expectancy at age 1 y.
†
Values refer to lower and upper bound.
Fig. 1. Survival curves (shaded areas), life expectancies (solid vertical lines), and ages at which only 5% of a synthetic same-sex cohort would still be alive
(dashed vertical lines) for seven high-mortality populations. For Trinidad, dashed survival curves and vertical lines with asterisks represent estimated upper
bounds. Source: authors’calculations based on published data from ref. 25 for Liberia, from ref. 26 for Trinidad, from ref. 28 for Ukraine, from ref. 31 for
Ireland, and from the Human Mortality Database (www.mortality.org) for Sweden and Iceland.
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life expectancy in Ireland before the famine was 38.3 y for both
men and women. No information pertaining to a condition of
freedom was available for the slave population in Trinidad.
Gender Difference in Mortality During the Crisis. Life expectancy
was higher for women than for men for all populations, with the
partial exception of the Trinidad slaves for whom, according to
the lower-bound life table, males lived slightly longer than fe-
males (Fig. 1 and Table 1). In the lower-bound life table, female
slaves suffered from higher mortality than male slaves from birth
until age 25 y, while in the upper-bound life table women had
lower mortality than men at birth, experienced higher mortality
until age 15 y, and afterward experienced lower mortality again.
The general survival advantage of women is also reflected in the
fact that the extreme age was higher for females than for males
for all populations (Fig. 1).
For five populations—the two Icelandic populations and the
Swedish, Irish, and Ukrainian populations—life expectancy esti-
mates were available before and after the crisis, thus permitting
evaluation of the absolute and relative impact of the crises for men
and women (Table 2). The absolute reduction in life expectancy
was higher for males than for females in Ireland; for the other four
populations the absolute reduction was greater for females (who
had higher life expectancies than males in these populations both
before and during the crisis). The relative decrease in life expec-
tancy was higher for males in the Ukraine and Ireland, higher for
females in Iceland, and roughly the same in Sweden.
The sex difference in life expectancy varied among the pop-
ulations (Table 1). Women among the freed slaves migrating
back to Liberia had the smallest absolute advantage over men
(0.55 y). In Trinidad, as mentioned above, males lived longer
than females (1.27 y) in the lower-bound scenario but not in
the upper-bound scenario, in which the advantage was in favor
of women (1.13 y). In relative terms, the largest advantages of
women over men during crisis were in the Ukraine (a differ-
ence of almost 50%), Liberia (33%), and Ireland (20%).
Among the other populations, the relative advantage was 12%
or smaller.
A decomposition of the difference in life expectancy by age
shows that the biggest contribution to these differentials comes
from strikingly large mortality differences between male and
female infants (Fig. 2). After age 1 y, mortality differences be-
tween the sexes contributed less and less to the total gap in life
expectancy. Table 3 reports the share of the contribution of the
0–1-y age group and all other ages together with the total sex
Table 2. Male and female decrease in life expectancy for five
high-mortality populations during extreme mortality conditions
Population
Male Female
Absolute, y Relative Absolute, y Relative
Ukraine, 1933 34.28 0.82 35.08 0.76
Ireland, 1845–1849 19.60 0.51 15.90 0.41
Iceland, 1846 17.49 0.49 21.99 0.54
Iceland, 1882 20.86 0.55 25.16 0.57
Sweden, 1773 15.16 0.47 16.40 0.46
Fig. 2. Age decomposition of the differences in life expectancies between males and females for the eight high-mortality populations. Light blue bars for
Trinidad represent the decomposition of the upper-bound life expectancy values. See Table S1.
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difference in life expectancy. In Liberia, Trinidad, Iceland, and
Ireland infants contributed more than half of the total difference
(with values in some cases reaching 70–80% of the difference).
In Sweden and the Ukraine, the shares of the 0–1-y age group
were around 20%, which still can be considered a substantial
contribution for a single age group compared with all other ages.
Women lived longer than men in almost all populations, and
higher proportions of women than men survived from birth to
each age (Fig. 1). The female survival curves were higher than
the male ones at all ages, with the exception of the Trinidad
slaves. Here survival was higher for men than for women until
age 50 y in the lower-bound scenario or until age 25 y in the
upper-bound scenario, as a consequence of the higher female
mortality in childhood and at young adult ages.
The absolute and relative differences in male vs. female mortality
at each age provide further details about sex differences in mortality
(Fig. 3). Within the study populations, the absolute and relative
differences mostly follow a similar age trajectory, so that both ab-
solute and relative differences are largest at similar ages. An ex-
ception is the Ukraine, where the absolute difference increases
sharply from age 40 y, whereas the relative difference increases
rapidly from birth to age 20 y and then decreases from age 60 y.
The ages at which the relative difference is highest vary. Al-
most all populations show a relative female survival advantage
across all ages, with the exception of Liberia and Trinidad. Here
males had a survival advantage at adult ages for Liberia (between
age 35 y and age 49 y), and at infant and juvenile ages for Tri-
nidad (until age 15 y or 25 y, for the upper- and lower-bound
estimates, respectively).
Discussion
The conditions experienced by the people in the analyzed pop-
ulations were horrific. Even though the crises reduced the female
survival advantage in life expectancy, women still survived better
than men. In all populations men had equal or higher mortality
than women across almost all ages. A substantial part of the
overall female advantage in life expectancy was due to survival
differences among infants. Further support for the hypothesis of
an overall ability of women to withstand high-mortality crises
better than men comes from a different mortality measure: For
all populations, the extreme age (the age to which 5% of the
population survived) was higher for females than for males.
A female survival advantage has also been documented in
more recent and less extreme famines. During the Dutch Hunger
Winter (33), the famines of Madras and Bombay (34), five south
Asian famines, the Bengal famine, and the famine in the Matlab
region (35), the overall effect of the crisis was greater for men
than for women, even in regions where women usually had
higher mortality than men. The Matlab famine did not signifi-
cantly affect neonatal mortality, which increased only slightly
(35). However, data on infants, especially during crises, must be
considered with caution: Infants’deaths could be underreported
when mortality increases (during a crisis) because more children
die at very young ages, which increases the probability of the
death not being reported (36).
In all populations under study, with the partial exception of
the Trinidad slaves (in the case of the lower-bound scenario),
females lived longer than males. These results indicate an im-
portant distinction: In populations that are exposed to harsh
famines and epidemics the female survival advantage holds at all
ages, whereas in slave populations in which stressors are or have
been under some human control, males can have higher life
expectancy and lower mortality than females, at least across
some ages.
The slaves of Trinidad differ from the other populations in
that their age-structure and mortality are heavily influenced by
the decisions of the slave owners. Among the Trinidad slaves,
young adult men had lower mortality than young women, per-
haps because a premium was placed on their survival. Several
studies show that male slaves employed in the plantations during
the 19th century had a higher monetary value than female slaves
(for both creole and African-born slaves) in the United States,
Cuba, the British West Indies, and Brazil; only occasionally did
female prices exceed those of males, namely, in urban areas,
where women were valued for domestic work (37–39). The
higher male mortality after age 15 y or age 25 y, depending on
whether the upper- or lower-bound scenario is considered, could
reflect their harder working conditions. A series of frequent re-
volts between 1638 and 1838 in the British Caribbean testifies to
very tough working conditions (40–42). For example, as late as
1823 the planters of Barbados refused a proposal to give the
slaves 1 d off per week, and those of Trinidad and British Guiana
rejected a document by the British governor which proposed,
among other things, a day off to permit religious instruction and
the abolition of the whip; the planters argued that the whip was
necessary to maintain discipline, and time for religious duties
would merely encourage idleness among the slaves (43). More-
over, to keep sugar mills and boilers operating 24 h a day, slaves
could work shifts up to 30 h long (25) while having minimal and
inadequate nutrition, affected by periodic severe dietary depri-
vation and occasional near starvation, as showed by physical
anthropological evidence (44). The skeletal and dental analyses
suggest an average life expectancy at birth of 29 y for a pop-
ulation of slaves in a sugar plantation of Barbados between
1660 and 1820 (44). However, the authors point out, this esti-
mate was severely biased by the highly inaccurate skeletal esti-
mate of infant survival (95% infant survival against slightly more
than 50% obtained from more accurate historical records for the
same population) (44), implying that the real life expectancy
value was much lower. Finally, the low life expectancy of the
slave population of Trinidad could also be the consequence of
Trinidad’s being one of the three Caribbean colonies with the
most rapidly expanding export sector in the 19th century. The
demand for newly arrived slaves was larger in these colonies than
in other colonies. Slaves just arrived in the Americas had lower
life expectancy than those born there or who had been there
already for some time, because of the adaptation period (called
“seasoning”) that lasted about 1 y after the arrival (45).
A similar explanation is not available for Liberia, where males
had lower mortality than females between ages 35 y and 49 y.
Several explanations can be hypothesized. The impossibility of
determining the gender for 4% of the records, as mentioned
above, might have caused some bias in the sex-specific death
rates. A second explanation could be related to the need to es-
tablish a stable colony in an initially very hostile environment,
which might have favored the individuals considered more im-
portant for this purpose, namely, adult men in the most productive
Table 3. Age-specific share, in percentage points, of the total
male–female difference in life expectancy for eight
high-mortality populations during extreme mortality conditions
Population Age 0–1 y, % Age >1y,%
Liberia, 1820–1843 77 23
Trinidad, 1813–1816 88–77* 12–23*
Ukraine, 1933 22 78
Sweden, 1773 20 80
Iceland, 1846 108
†
−8
†
Iceland, 1882 53 47
Ireland, 1845–1849 67 33
*Values refer to lower and upper bound.
†
The values of 108% and −8% are explained by the fact that the contribu-
tion of the 0–1 age group to the sex gap in life expectancy was 1.033 y in
favor of women, while the overall difference was 0.96 y because other ages
contributed with negative values (in favor of men).
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ages. Overall males in Liberia still had a lower life expectancy
than females because adult ages contributed little to life expec-
tancy, as shown by the age decomposition analysis.
A specific distribution of causes of death could partly explain
these patterns, but this appears not to have been the case among
the Liberian former slaves. This population is the only one for
which we have a detailed list of causes of death. The registration
shows that the distribution of causes of death is similar for males
and females, except for deaths due to childbirth and gyneco-
logical diseases, which obviously affected only women, and
deaths due to accident and violence, which were higher among
men. This suggests another important point of discussion: that in
times of crises, the well-known propensity of men more than
women to die from accidents or violence could partly be the
cause for their higher mortality. However, this is hard to assess
with certainty because of the lack of data on causes of death in
all the other cases (and for crisis populations in general). Sec-
ondly, if men die more from these causes, women still die from
childbirth and gynecological diseases that affect women exclu-
sively, even in a context of reduced fertility such as that char-
acterized by critical mortality conditions.
At the onset of famines and epidemics, the age structures of
the populations at risk were shaped by previous mortality, fer-
tility, and migration patterns. The risk of death then suddenly
rose to extreme levels for everyone. Under these conditions, the
youngest ages contributed the most to sex differences in life
expectancy. The importance of infant and early childhood mor-
tality was particularly striking in the two Icelandic epidemics.
This was expected, as measles is mostly a childhood disease.
Moreover, Iceland is the only case in which mortality increased
relatively more for women than for men. This is consistent with
the pattern of sex difference in measles survival: Excess female
measles mortality is found worldwide (46), even with equal
vaccination rates for both sexes, lower incidence of measles for
girls than for boys, and higher and longer-persisting antibody
titers on vaccination among girls than among boys (47, 48). It was
Fig. 3. Male:female mortality ratios and differences over age for seven high-mortality populations. Gray lines represent the unsmoothed data, blue lines
represent the smoothed data [obtained with the R function stat_smooth (100)], and the gray shaded areas represent the SEs of the smoothing. The dashed
lines for Trinidad represent the smoothed upper-bound values. Source: authors’calculations based on published data from ref. 25 for Liberia, from ref. 26 for
Trinidad, from ref. 28 for Ukraine, from ref. 31 for Ireland, and from the Human Mortality Database (www.mortality.org) for Sweden and Iceland.
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hypothesized that this difference could be due to different
treatments by sex or to higher intensity of exposure for females,
but studies from rural Senegal and Guinea Bissau showed no
sign of sex bias in health maintenance, nutritional status, or
breastfeeding patterns (49–52). Moreover, for the age group 5–
14 y (in which most measles cases are contracted in school),
conditions were similar for boys and girls, and among those in-
fected at home boys and girls had the same mean intensity of
exposure (49). Further investigations have led to the discovery
that the administration of the high-titer measles vaccine at age
4–5 mo and the resulting shift of diphtheria, pertussis, and tet-
anus (DTP) and inactivated polio vaccine (IPV) administration
after measles vaccination were associated with an increased fe-
male:male mortality ratio compared with the administration of
the medium-titer measles vaccine at 9 mo, which usually comes
after the DTP and IPV vaccines. These findings highlight a dif-
ferential effect of vaccines depending on sex and sequence of
vaccinations (53, 54). The relative increase of mortality in the
two Icelandic epidemics was indeed bigger for women than for
men, especially at young ages; this is the only case where this
happens among the seven populations considered in this study.
However, women still showed a marked overall survival advan-
tage, and the survival advantage of girls contributed the most to
the entire gender difference in life expectancy.
Infant age affects life expectancy the most when infant mortality
is high, even in noncrisis years. However, it is striking that during
epidemics and famines as harsh as those analyzed here newborn
girls still survived better than newborn boys. Even in Liberia, the
population with the lowest life expectancy, newborn girls were
hardier than newborn boys. That females survived more than
males even at the infant ages, when behavioral differences are
minimal, lends support to a biological underpinning of the female
survival advantage. However, even if behavioral differences among
infants are small, parents can have different attitudes toward
children, depending on their sex. Studies show that in preindustrial
Europe, the addition of a child increased the parents’mortality
risk when resources were scarce and had to be shared. If the child
was a boy, the mortality increase was the same for both mother
and father, but if the child was a girl, the father’s risk did not
increase because he was not willing to share resources with an
additional girl, while the mother’s risk increased even more (55).
Other studies found that the number of sons or daughters born or
raised to adulthood had no effect on paternal longevity but did
affect maternal postreproductive longevity (56) and that, irre-
spective of access to resources, having many sons reduced the
survival of mothers but not of fathers (57).
Except for the slave population of Trinidad, in all the other
cases starvation dysentery and diarrhea are likely to have been
major causes of death. These causes are strongly associated with
nutritional status, and therefore, the allocation of food might
have played a key role in shaping the survival patterns. Evidence
suggests that sudden changes in availability of food may not in-
fluence infant mortality during crises characterized by nutri-
tionally related diseases: When the mother breastfeeds, the
infant is protected because breast milk appears to be sufficient
until the mother is nearly starved (58). For other age groups,
studies on practices of resource allocation from various areas of
preindustrial Europe show a penalty for women, especially at
young ages. From 1775 to 1850 the preexisting female excess
mortality between the ages of 1 and 14 y increased sharply,
mostly due to discrimination in the resource allocation within the
household (55, 59).
Widespread social practices could be disrupted by famines or
epidemics. Famines were often accompanied by prostitution,
child abandonment or infanticide, aberrant food practices, and
massive migration flows (60). Some of these factors could act in
favor of women; others could be detrimental to them. While
increased prostitution rates and migration could partly contrib-
ute to the higher survival chances of women (through prostitu-
tion women are able to get extra resources; migration reduces
the pressure on the scarce resources, thus offering some relief
from hunger to those who stay, composed mostly of women,
children, and the elderly), child abandonment and infanticide, at
least in some cases, could favor boys at the expenses of girls (60).
Various stories from different crises testify to maternal resilience
and tell of mothers taking extreme actions that led to the survival
of both mother and infant (61–63).
A growing body of research on sex differences in mortality and
immunoresponse among humans and other mammals supports
the fundamentally biological foundations of sex differences in
human mortality. Biological factors include hormonal and
chromosomal genetic differences. Sex hormones seem to play a
key role (64, 65): estrogens have antiinflammatory, vasoprotective
effects (66–68), whereas testosterone seems to increase the mor-
tality risk for certain diseases (69, 70), although the evidence on
this point is mixed (71, 72). Moreover, while estrogens enhance
immune defenses, testosterone and progesterone may have
immunosuppressive effects (73–75). The presence of two X-
chromosomes may pose a further advantage with respect to
specific X-linked diseases (e.g., hemophilia A) due to an ame-
lioration of harmful gene mutations through nonmutated alleles
on the other X-chromosome. The possibility of having two dif-
ferent alleles on the two X-chromosomes further contributes to
the physiological diversity that can be advantageous when en-
countering new immune challenges (76–79).
Females live longer than males in humans and in the large
majority of monkeys and apes for which data are available, in
both captive and wild populations (80). Mammalian females
generally outlive males in species in which males compete with
each other for opportunities to mate (81, 82). This occurs in
polygynous species and is commonly accompanied by sexual di-
morphism in body size, which helps males compete for females.
The sexual dimorphism in human body size indicates that our
evolutionary history contained a long period of polygynous re-
production (83). Furthermore, the ratio of testes to body size is
larger in polygynously mating species than in monogamous
species (84). The relative testes size of humans in comparison
with other species is further evidence that humans mated poly-
gynously during their evolutionary history (84). Therefore, from
an evolutionary perspective, the observed sex differences in hu-
man mortality are not exceptional; instead, humans fall well
within the range of sex differences observed in other mammal
species (81, 82, 85). Furthermore, among vertebrates males are
more likely to be infected with parasites and to carry a greater
intensity of infection than female conspecifics (86). It has been
argued that this is due to an immunosuppressive effect of tes-
tosterone (86), but evidence is mixed (87). An alternative expla-
nation comes from one experimental study that points toward a
role of testosterone in altering social behavior so as to increase
exposure to infection rather than the hormone acting as an im-
munosuppressant (88). It has further been argued that increases in
Darwinian fitness accompanying a higher investment in the im-
mune system in females, but not in males, may be sufficient to
explain the observed sex differences in immune response (87, 89).
Female mammals not only seem to be better at dealing with in-
fection but also survive better than male mammals under harsh
environmental conditions—an observation confirmed by a large
comparative study on 26 ungulate populations (90), among others.
Research has also provided evidence for an apparent female
advantage in immune protection among humans: The incidence
of many bacterial, viral, parasitic, and fungal infectious diseases
(e.g., leptospirosis, chistosomiasis, brucellosis, rabies, leishman-
iasis, pulmonary tuberculosis, hepatitis A, meningococcal and
pneumococcal infections, and seasonal influenza) is substantially
higher in men than in premenopausal women. This suggests that
progesterone and testosterone have mainly immunosuppressive
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effects, whereas estrogens enhance immune defenses (73–75)
and act as antioxidant (91). Moreover, autoimmune diseases are
more prevalent in women than in men, as is a stronger immune
response to vaccinations (74, 92). These findings led researchers
to conclude that low male immunocompetence contributes to sex
differences in mortality (93), but the mechanisms through which
sex hormones affect immune responses in humans have not been
fully elucidated.
Additionally, behavioral factors have been identified as im-
portant determinants of the male–female survival difference in
contemporary populations (94, 95). The high preponderance of
risk-taking behaviors among men contributes substantially to the
sex gap in life expectancy. Men consume tobacco, alcohol, and
psychoactive substances in greater quantities, drive less safely,
and eat less salubriously than women do; this results in elevated
risks of cardiovascular diseases, lung cancer, liver cirrhosis, and
accident fatalities (96, 97). In high-income countries cigarette
smoking has been identified as the largest factor contributing to
the mortality differential (98, 99). However, although behaviors
are important factors, they cannot fully explain the sex difference
in survival, as suggested by the fact that some female advantage
is found among nonsmokers (6, 7), devout Mormons (8), and
Catholic nuns vs. monks (9).
In almost all human populations women live longer than men. In
this study we found that the female survival advantage extends to
seven documented populations experiencing high-mortality crises.
Our results add another piece to the puzzle of gender differences in
survival. They suggest that the female advantage stems from fun-
damental biological roots and is influenced by socially and envi-
ronmentally determined risks, opportunities, and resources.
1. Austad SN (2006) Why women live longer than men: Sex differences in longevity.
Gend Med 3:79–92.
2. Barford A, Dorling D, Davey Smith G, Shaw M (2006) Life expectancy: Women now
on top everywhere. BMJ 332:808.
3. Glei DA, Horiuchi S (2007) The narrowing sex differential in life expectancy in high-
income populations: Effects of differences in the age pattern of mortality. Popul
Stud (Camb) 61:141–159.
4. Kalben BB (2000) Why men die younger: Causes of mortality differences by sex. N
Am Actuar J 4:83–111.
5. Wachter KW (2008) Biodemography comes of age. Demogr Res 19:1501–1512.
6. Brønnum-Hansen H, Juel K (2001) Abstention from smoking extends life and com-
presses morbidity: A population based study of health expectancy among smokers
and never smokers in Denmark. Tob Control 10:273–278.
7. Rogers RG, Powell-Griner E (1991) Life expectancies of cigarette smokers and non-
smokers in the United States. Soc Sci Med 32:1151–1159.
8. Lindahl-Jacobsen R, et al. (2013) The male-female health-survival paradox and sex
differences in cohort life expectancy in Utah, Denmark, and Sweden 1850-1910. Ann
Epidemiol 23:161–166.
9. Luy M (2003) Causes of male excess mortality: Insights from cloistered populations.
Popul Dev Rev 29:647–676.
10. Boco AG (2015) Assessing sex differentials in under-five mortality in sub-Saharan
Africa: A cross-national comparative analysis. Can Stud Popul 41:49–87.
11. Drevenstedt GL, Crimmins EM, Vasunilashorn S, Finch CE (2008) The rise and fall of
excess male infant mortality. Proc Natl Acad Sci USA 105:5016–5021.
12. Ulizzi L, Zonta LA (2002) Sex differential patterns in perinatal deaths in Italy. Hum
Biol 74:879–888.
13. Grayson DK (1993) Differential mortality and the Donner Party disaster. Evol
Anthropol Issues News Rev 2:151–159.
14. Ferlay J, et al. (2013) Cancer incidence and mortality patterns in Europe: Estimates
for 40 countries in 2012. Eur J Cancer 49:1374–1403.
15. Isaksson R-M, et al. (2011) Better long-term survival in young and middle-aged
women than in men after a first myocardial infarction between 1985 and 2006.
An analysis of 8630 patients in the northern Sweden MONICA study. BMC Cardiovasc
Disord 11:1–8.
16. Jagger C, et al.; EHLEIS team (2008) Inequalities in healthy life years in the
25 countries of the European Union in 2005: A cross-national meta-regression
analysis. Lancet 372:2124–2131.
17. Nusselder WJ, Looman CW, Van Oyen H, Robine JM, Jagger C (2010) Gender dif-
ferences in health of EU10 and EU15 populations: The double burden of EU10 men.
Eur J Ageing 7:219–227.
18. Van Oyen H, et al. (2010) Gender gaps in life expectancy and expected years with
activity limitations at age 50 in the European Union: Associations with macro-level
structural indicators. Eur J Ageing 7:229–237.
19. Andersen KK, Andersen ZJ, Olsen TS (2011) Predictors of early and late case-fatality
in a nationwide Danish study of 26,818 patients with first-ever ischemic stroke.
Stroke 42:2806–2812.
20. Koek HL, et al. (2006) Short- and long-term prognosis after acute myocardial in-
farction in men versus women. Am J Cardiol 98:993–999.
21. Nielsen S, et al. (2014) Sex-specific trends in 4-year survival in 37 276 men and
women with acute myocardial infarction before the age of 55 years in Sweden,
1987-2006: A register-based cohort study. BMJ Open 4:e004598.
22. Preston SH, McDaniel A, Grushka C (1993) New model life tables for high-mortality
populations. Hist Methods 26:149–159.
23. McDaniel A, Preston SH (1994) Patterns of mortality by age and cause of death
among nineteenth-century immigrants to Liberia. Popul Stud (Camb) 48:99–115.
24. McDaniel A (1992) Extreme mortality in nineteenth-century Africa: The case of Li-
berian immigrants. Demography 29:581–594.
25. John AM (1988) Plantation slave mortality in Trinidad. Popul Stud (Camb) 42:
161–182.
26. John AM (1988) The Plantation Slaves of Trinidad, 1783-1816: A Mathematical and
Demographic Enquiry (Cambridge Univ Press, New York).
27. Meslé F, Vallin J (2012) Mortality and Causes of Death in 20th-Century Ukraine
(Springer Science & Business Media, New York).
28. Dribe M, Olsson M, Svensson P (2015) Famines in the Nordic countries, AD 536-1875.
Lund Papers in Economic History. General Issues (138) (Lund University, Lund,
Sweden).
29. Cliff AD, Haggett P, Graham R (1983) Reconstruction of diffusion at local scales: The
1846, 1882 and 1904 measles epidemics in northwest Iceland. J Hist Geogr 9:347–368.
30. Boyle PP, O Gráda C (1986) Fertility trends, excess mortality, and the Great Irish
Famine. Demography 23:543–562.
31. Preston S, Heuveline P, Guillot M (2001) Demography: Measuring and Modeling
Population Processes (Blackwell Publishers, Oxford).
32. Andreev EM, Shkolnikov VM, Begun AZ (2002) Algorithm for decomposition of
differences between aggregate demographic measures and its application to life
expectancies, healthy life expectancies, parity-progression ratios and total fertility
rates. Demogr Res 7:499–522.
33. Dols MJ, Van Arcken DJ (1946) Food supply and nutrition in the Netherlands during
and immediately after World War II. Milbank Mem Fund Q 24:319–358.
34. McAlpin MB (2014) Subject to Famine: Food Crisis and Economic Change in Western
India, 1860-1920 (Princeton Univ Press, Princeton).
35. Dyson T (1991) On the demography of south Asian famines. Part II. Popul Stud
(Camb) 45:279–297.
36. Menken J, Campbell C (1992) Age-patterns of famine-related mortality increase:
Implications for long-term population growth. Health Transit Rev 2:91–101.
37. Lennon C (2016) Slave escape, prices, and the fugitive slave act of 1850. J Law Econ
59:669–695.
38. Olmstead AL, Rhode PW (2008) Creating Abundance: Biological Innovation and
American Agricultural Development (Cambridge Univ Press, New York).
39. Fraginals MM, Klein HS, Engerman SL (1983) The level and structure of slave prices
on Cuban plantations in the mid-nineteenth century: Some comparative perspec-
tives. Am Hist Rev 88:1201–1218.
40. Beckles H (1988) Caribbean anti-slavery the self-liberation ethos of enslaved Blacks.
J Caribb Hist 22:1–19.
41. Hart R (1980) Slaves Who Abolished Slavery: Blacks in Bondage (Institute of Social
and Economic Research, Kingston, Jamaica), Vol 1.
42. Hart R (1985) Slaves Who Abolished Slavery: Blacks in Rebellion (Institute of Social
and Economic Research, Kingston, Jamaica), Vol 2.
43. Williams E (1944) Capitalism and Slavery (Univ of North Carolina Press, Chapel Hill,
NC).
44. Handler JS, Corruccini RS (1983) Plantation slave life in Barbados: A physical an-
thropological analysis. J Interdiscip Hist 14:65–90.
45. Eltis D, Lewis FD, Richardson D (2005) Slave prices, the African slave trade, and
productivity in the Caribbean, 1674–1807. Econ Hist Rev 58:673–700.
46. Garenne M (1994) Sex differences in measles mortality: A world review. Int J
Epidemiol 23:632–642.
47. Garenne M (2015) Demographic evidence of sex differences in vulnerability to in-
fectious diseases. J Infect Dis 211:331–332.
48. Muenchhoff M, Goulder PJ (2014) Sex differences in pediatric infectious diseases.
J Infect Dis 209(Suppl 3):S120–S126.
49. Garenne M, Aaby P (1990) Pattern of exposure and measles mortality in Senegal.
J Infect Dis 161:1088–1094.
50. Svedberg P (1990) Undernutrition in Sub‐Saharan Africa: Is there a gender bias?
J Dev Stud 26:469–486.
51. Aaby P, et al. (1993) Long-term survival after Edmonston-Zagreb measles vaccination
in Guinea-Bissau: Increased female mortality rate. J Pediatr 122:904–908.
52. Aaby P, et al. (1994) Sex-specific differences in mortality after high-titre measles
immunization in rural Senegal. Bull World Health Organ 72:761–770.
53. Aaby P, et al. (2007) Increased female-male mortality ratio associated with in-
activated polio and diphtheria-tetanus-pertussis vaccines: Observations from vacci-
nation trials in Guinea-Bissau. Pediatr Infect Dis J 26:247–252.
54. Aaby P, et al. (2010) Sex differences in the effect of vaccines on the risk of hospi-
talization due to measles in Guinea-bissau. Pediatr Infect Dis J 29:324–328.
55. Klasen S (1998) Marriage, bargaining, and intrahousehold resource allocation: Excess
female mortality among adults during early German development, 1740–1860.
J Econ Hist 58:432–467.
56. Helle S, Lummaa V, Jokela J (2002) Sons reduced maternal longevity in preindustrial
humans. Science 296:1085.
8of9
|
www.pnas.org/cgi/doi/10.1073/pnas.1701535115 Zarulli et al.

57. Helle S, Lummaa V (2013) A trade-off between having many sons and shorter ma-
ternal post-reproductive survival in pre-industrial Finland. Biol Lett 9:20130034.
58. McCann MF, Liskin LS, Piotrow PT, Rinehart W, Fox G (1981) Breast-feeding, fertility,
and family planning. Popul Rep J J525–J575.
59. McNay K, Humphries J, Klasen S (2005) Excess female mortality in nineteenth-
century England and Wales. Soc Sci Hist 29:649–681.
60. Gráda CÓ (2009) Famine: A Short History (Princeton Univ Press, Princeton).
61. Edgerton-Tarpley K (2004) Family and gender in famine: Cultural responses to di-
saster in North China, 1876-1879. J Womens Hist 16:119–147.
62. Greenough PR (1982) Prosperity and Misery in Modern Bengal: The Famine of 1943-
1944 (Oxford Univ Press, Oxford).
63. Hodges S (2005) ‘Looting’the lock hospital in colonial Madras during the famine
years of the 1870s. Soc Hist Med 18:379–398.
64. Vaccarino V, et al. (2010) Ischaemic heart disease in women: Are there sex differ-
ences in pathophysiology and risk factors? Position paper from the Working Group
on Coronary Pathophysiology and Microcirculation of the European Society of
Cardiology. Cardiovasc Res 90:9–17.
65. Waldron I (1983) Sex differences in human mortality: The role of genetic factors. Soc
Sci Med 17:321–333.
66. Xing D, Nozell S, Chen Y-F, Hage F, Oparil S (2009) Estrogen and mechanisms of
vascular protection. Arterioscler Thromb Vasc Biol 29:289–295.
67. Babiker FA, et al. (2002) Estrogenic hormone action in the heart: Regulatory network
and function. Cardiovasc Res 53:709–719.
68. Wise PM, Dubal DB, Rau SW, Brown CM, Suzuki S (2005) Are estrogens protective or
risk factors in brain injury and neurodegeneration? Reevaluation after the women’s
health initiative. Endocr Rev 26:308–312.
69. Haring R, et al. (2010) Low serum testosterone levels are associated with increased
risk of mortality in a population-based cohort of men aged 20–79. Eur Heart J 31:
1494–1501.
70. Holmegard HN, Nordestgaard BG, Jensen GB, Tybjærg-Hansen A, Benn M (2016) Sex
hormones and ischemic stroke: A prospective cohort study and meta-analyses. J Clin
Endocrinol Metab 101:69–78.
71. Ruige JB, Mahmoud AM, De Bacquer D, Kaufman JM (2011) Endogenous testos-
terone and cardiovascular disease in healthy men: A meta-analysis. Heart 97:
870–875.
72. Schooling CM (2016) Could androgens be relevant to partly explain why men have
lower life expectancy than women? J Epidemiol Community Health 70:324–328.
73. Bouman A, Heineman MJ, Faas MM (2005) Sex hormones and the immune response
in humans. Hum Reprod Update 11:411–423.
74. Giefing-Kröll C, Berger P, Lepperdinger G, Grubeck-Loebenstein B (2015) How sex
and age affect immune responses, susceptibility to infections, and response to vac-
cination. Aging Cell 14:309–321.
75. Pennell LM, Galligan CL, Fish EN (2012) Sex affects immunity. J Autoimmun 38:
J282–J291.
76. Morris JA, Harrison LM (2009) Hypothesis: Increased male mortality caused by in-
fection is due to a decrease in heterozygous loci as a result of a single X chromo-
some. Med Hypotheses 72:322–324.
77. Spolarics Z (2007) The X-files of inflammation: Cellular mosaicism of X-linked poly-
morphic genes and the female advantage in the host response to injury and in-
fection. Shock 27:597–604.
78. Christensen K, et al. (2000) X-linked genetic factors regulate hematopoietic stem-cell
kinetics in females. Blood 95:2449–2451.
79. Christensen K, Ørstavik KH, Vaupel JW (2001) The X chromosome and the female
survival advantage: An example of the intersection between genetics, epidemiology
and demography. Ann N Y Acad Sci 954:175–183.
80. Austad SN, Fischer KE (2016) Sex differences in lifespan. Cell Metab 23:1022–1033.
81. Clutton-Brock TH, Isvaran K (2007) Sex differences in ageing in natural populations
of vertebrates. Proc Biol Sci 274:3097–3104.
82. Promislow DE (1992) Costs of sexual selection in natural populations of mammals.
Proc R Soc Lond B Biol Sci 247:203–210.
83. Mitani JC, Gros-Louis J, Richards AF (1996) Sexual dimorphism, the operational sex
ratio, and the intensity of male competition in polygynous primates. Am Nat 147:
966–980.
84. Harcourt AH, Harvey PH, Larson SG, Short RV (1981) Testis weight, body weight and
breeding system in primates. Nature 293:55–57.
85. Colchero F, et al. (2016) The emergence of longevous populations. Proc Natl Acad Sci
USA 113:7681–7690.
86. Zuk M, McKean KA (1996) Sex differences in parasite infections: Patterns and pro-
cesses. Int J Parasitol 26:1009–1023.
87. Nunn CL, Lindenfors P, Pursall ER, Rolff J (2009) On sexual dimorphism in immune
function. Philos Trans R Soc Lond B Biol Sci 364:61–69.
88. Grear DA, Perkins SE, Hudson PJ (2009) Does elevated testosterone result in in-
creased exposure and transmission of parasites? Ecol Lett 12:528–537.
89. Roth O, Scharsack JP, Keller I, Reusch TB (2011) Bateman’s principle and immunity in
a sex-role reversed pipefish. J Evol Biol 24:1410–1420.
90. Toïgo C, Gaillard JM (2003) Causes of sex‐biased adult survival in ungulates: Sexual
size dimorphism, mating tactic or environment harshness? Oikos 101:376–384.
91. Moosmann B, Behl C (1999) The antioxidant neuroprotective effects of estrogens
and phenolic compounds are independent from their estrogenic properties. Proc
Natl Acad Sci USA 96:8867–8872.
92. Whitacre CC (2001) Sex differences in autoimmune disease. Nat Immunol 2:777–780.
93. Owens IPF (2002) Ecology and evolution. Sex differences in mortality rate. Science
297:2008–2009.
94. Lemaire J (2002) Why do females live longer than males? N Am Actuar J 6:21–37.
95. Thorslund M, Wastesson JW, Agahi N, Lagergren M, Parker MG (2013) The rise and
fall of women’s advantage: A comparison of national trends in life expectancy at age
65 years. Eur J Ageing 10:271–277.
96. Waldron I (1985) What do we know about causes of sex differences in mortality? A
review of the literature. Popul Bull UN 59–76.
97. Wardle J, et al. (2004) Gender differences in food choice: The contribution of health
beliefs and dieting. Ann Behav Med 27:107–116.
98. Preston SH, Wang H (2006) Sex mortality differences in the United States: The role of
cohort smoking patterns. Demography 43:631–646.
99. Beltrán-Sánchez H, Finch CE, Crimmins EM (2015) Twentieth century surge of excess
adult male mortality. Proc Natl Acad Sci USA 112:8993–8998.
100. R Core Team (2013) R: A language and environment for statistical computing (R
Foundation for Statistical Computing, Vienna), Version 3.3.3.
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