Previous Article |
Table of Contents
Max Planck Institute for Demographic Research, Doberanerstrasse
114, 18057 Rostock, Germany
Edited by Kenneth W. Wachter, University of California, Berkeley,
CA, and approved November 16, 2000 (received for review September 8, 2000)
Month of birth influences adult life expectancy at ages 50+. Why?
In two countries of the Northern Hemisphere-Austria and Denmark-people born in autumn (October-December) live longer than those born in spring (April-June). Data for Australia show that, in
the Southern Hemisphere, the pattern is shifted by half a year. The
lifespan pattern of British immigrants to Australia is similar to that
of Austrians and Danes and significantly different from that of
Australians. These findings are based on population data with more than
a million observations and little or no selectivity. The differences in
lifespan are independent of the seasonal distribution of deaths and the
social differences in the seasonal distribution of births. In the
Northern Hemisphere, the excess mortality in the first year of life of
infants born in spring does not support the explanation of selective
infant survival. Instead, remaining life expectancy at age 50 appears
to depend on factors that arise in utero or early in
infancy and that increase susceptibility to diseases later in life.
This result is consistent with the finding that, at the turn of the
last century, infants born in autumn had higher birth weights than
those born in other seasons. Furthermore, differences in adult lifespan
by month of birth decrease over time and are significantly smaller in
more recent cohorts, which benefited from substantial improvements in
maternal and infant health.
Remarkable reductions in
old-age mortality over the past half century have fueled rapid growth
of the elderly population and have led to a substantial increase in
life expectancy (1). Yet we still have only limited knowledge about the
factors that affect mortality and survival in old age (2). Recent
research highlights the role of early-life factors that affect
late-life mortality (3). In particular, environmental conditions during the prenatal and early postnatal period have been found to influence adult health and mortality significantly (4, 5) although these results
are still controversial (6, 7).
We conjectured that the month of birth may be an indicator for
environmental factors that are linked to the seasons of the year. If
this conjecture is true, then the patterns of two geographically close
populations should resemble each other, and the pattern in the Northern
Hemisphere should be mirrored in the Southern Hemisphere. Furthermore,
lifespans of people who were born in the Northern Hemisphere but who
died in the Southern Hemisphere should resemble the pattern of the
Northern Hemisphere.
We obtained data on the populations of Denmark, Austria, and Australia
to test our conjecture. For Denmark, the longitudinal data are based on
the population register, which follows every person living in Denmark
from 1968 to the present. For Austria and Australia, we used
information from death certificates for all deaths that occurred in
1988-1996 and 1993-1997, respectively.
We have found that month of birth and remaining life expectancy at age
50 are related. We tested four hypotheses to explain the relationship.
The first hypothesis assumes that the interaction between age and the
seasons of mortality causes the differences in lifespan by month of
birth. For example, people born in April are older than people born in
November when the high mortality of winter strikes them. The second
hypothesis tests whether the differences are due to unobserved social
factors that influence or result from the seasonal timing of births.
The third hypothesis explains the differences in adult lifespan by
differential survival in the first year of life, whereas the fourth
hypothesis assumes that debilitation in utero or in the
first year of life increases the infant's susceptibility to diseases
at adult ages.
The Danish data consist of a mortality follow-up of all Danes who
were at least 50 years old on 1 April 1968; 1,371,003 people were
followed up to week 32 of 1998. The study excludes 1,994 people who
were lost to the registry during the observation period. Among those
who are included in the study, 86% (1,176,383 individuals) died before
week 32 of 1998; 14% (192,626 individuals) were still alive at the end
of the follow-up.
Exact dates of birth and death are known for a total of 681,677 Austrians who died between 1988 and 1996 and for 219,820 native-born Australians who died between 1993 and 1997 at ages 50+. Similar information was available for 43,074 people born in Britain who died in Australia.
For Denmark, remaining mean life expectancy at age 50 was calculated on
the basis of life tables that were corrected for left truncation. The
correction was achieved by calculating occurrence and exposure matrices
that take into account an individual's age on 1 April 1968. For
example, a person who was 70 at the beginning of the study and who died
at age 80 enters the exposures for ages 70 to 80 but is not included in
the exposures for ages 50 to 69. The central age-specific death rate is
based on the occurrence-exposure matrix. The corresponding life-table
death rate is derived by the Greville Method (8). For Austria and
Australia, we estimated remaining lifespan at age 50 by calculating the
average of the exact ages at death. We do not have populations at risk
for these two countries and therefore cannot calculate mortality rates
for them. We used t tests to perform pairwise comparisons
between mean age at death by quarter of birth. By using the Bonferroni method, the To test whether the seasonal difference in the risk of death accounts
for the differences in adult lifespan by month of birth, we calculated
the monthly deviations from the annual death rates. Given the data we
have, we were able to do this only for Denmark. Let x denote
age in integer years and let y denote current year. Let
i denote age in months since last birthday and let
j denote current month; let
D
Social Sciences
Lifespan depends on month of birth
Abstract
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
Introduction
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
Data and Methods
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
level of each individual test is adjusted downwards by
the number of tests to ensure that the overall risk for a number of
tests remains 0.05.
where
[ 1 ]
The surface of the logarithm of the monthly deviations from the
annual death rates is modeled by the equation
[ 2 ]
The variables I1 to
I5 are indicator variables. They take
the value 1 for a particular characteristic and zero otherwise. I1k indicates month of birth
(reference month: January), I2k
current month (reference month: January),
I3k age since last birthday in months
rounded up to the nearest integer (reference age: one month),
I4 sex (reference sex: males), and I5 cohort (reference birth cohort:
born between 1889 and 1918; other cohort born before 1889). The
parameter values a0,
ak,
bk, ck,
[ 3 ]
k, and
j are estimated by applying weighted least
squares; the weights are the exposed population
Tij. The term
jI5
0, and
0 are set equal to zero.
To test whether selective survival or debilitation during the first year of life explains the differences in remaining life expectancy at age 50, we used data on infant mortality for Denmark aggregated over the five-year period 1911-1915. The tabulations in the "Statistik tabelvaerk " provide number of deaths by age of the infant in months and month of death. Thus, the actual date of birth of the deceased infants can fall within a period of 2 months and on average falls on the first day of each month. On the basis of the monthly number of births aggregated over the period 1911-1915, we then calculated death rates for each month of the first year of life. To center the number of births at the first day of each month we used the average number of births of two adjacent months in our calculations.
To estimate the impact of month of birth on the monthly death rates in
the first year of life, we used a similar model as for adult ages. We
modeled the logarithm of the monthly deviations from the annual death
rate by applying Eq. 4:
|
[ 4 ] |
kln x I2k
corrects for the interaction in the death rates between age and current
month. The infant mortality surface consists of only 144 data points
(12 birth months × 12 ages). Monthly births are available only
for both sexes combined, and infant deaths by age in months and month
of death are aggregated over the 5-year period 1911-1915. Hence, the
model does not include sex and cohort effects. The parameter values
a0,
ak,
bk, c1, and
k were estimated by least squares regression.
| |
Results |
|---|
|
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
|
|---|
Differences in Adult Lifespan by Month of Birth. We find a similar relationship between month of birth and lifespan in both of our Northern Hemisphere countries. Adults born in autumn (October-December) live longer than those born in spring (April-June). The difference in lifespan between the spring and autumn born is twice as large in Austria (0.6 years) as in Denmark (0.3 years).
In Denmark, remaining life expectancy at age 50 is 27.52 years. For those born in the second quarter, lifespans are 0.19 ± 0.05 years shorter than average; for those born in the fourth quarter they are 0.12 ± 0.04 years longer than average (Fig. 1, green line). This difference is statistically significant (Cox-Mantel statistic: P < 0.001).
|
0.0238 = 0.98) but significant
(P < 0.001).
|
Seasonal Distribution of Deaths. The first hypothesis we tested assumes that the observed relationship is caused by the interaction of the seasonal distribution of deaths and the monthly increase in adult mortality. The longitudinal nature of the Danish data permits simultaneous modeling of cohort effects (i.e., month of birth) and period effects (i.e., current month) corrected for the monthly increase in mortality over a "12 months" period. We constructed birth cohorts by year and month of birth and calculated the deviation of their monthly death rates from their annual death rates between April 1968 and August 1998. This procedure standardizes for the yearly increase with age in mortality and results in a surface of relative deviations over age and time. The deviations depend on the current month (i.e., month of death), the age in months over a "12 months" period, and sex. As expected, we find that mortality increases by about 10% for males and females within 12 months (Fig. 2A, green lines). Mortality is lowest in August and highest in January; the difference is about 17.6% for males and 13.9% for females (Fig. 2A, red lines). The maximum difference in the death rates by month of birth is approximately 5.3% for males and 4.5% for females (Fig. 2A, blue lines). The monthly pattern is similar for females (Fig. 2A, solid lines) and males (Fig. 2A, dashed lines).
Although all three factors examined influence adult mortality simultaneously (Fig. 2B), they do not influence each other. Results do not change when the impact of the three factors is estimated simultaneously by using a multivariate regression model. The model permits closer examination of the interaction between the monthly increase in adult mortality, the seasonality of death, and the differences by month of birth. It reveals that considerable differences do indeed exist in age-specific mortality trajectories over a year of life depending on the month of birth (Fig. 2C). However, over a year of life, the differences by season of death cancel each other out almost entirely whereas the differences by month of birth remain. For parameter estimates see supplemental Table 1.Social Differences in the Seasonal Distribution of Births. The second hypothesis we tested assumes that the causal mechanism is linked to socio-economic differences in the seasonal distribution of births: it may be that comparatively more people who belong to higher social classes are born in autumn. In Austria and Denmark, births are distributed seasonally over the year. Both social and biological factors influence this seasonality (19, 20). If the seasonality is partly driven by the preference of couples for certain seasons, then the intensity of the preference may differ between social groups.
We used education as an indicator of social group. Parental education is not contained on birth certificates in the beginning of the 20th century. We assumed that in 1991 a person's educational level was closely linked to the educational level of his or her parents. The Austrian microcensus of the year 1991 reveals that the educational status of 15- to 19-year-old Austrians depends to a large part on the social and educational status of the parents (21), despite tuition-free access to all levels of education since the 1970s. Thus, for earlier birth cohorts that did not benefit from the expansion of the Austrian educational system, the intergenerational correlation in education must have been even stronger than it is today. We then calculated the seasonal distribution of the birth dates of Austrians aged 50+ by educational group on the basis of the 1991 census. We found that the spring peak in births is stronger among adults with high or medium education whereas those with basic education are over-represented among the autumn born (Fig. 3). The correlation between mean age at death by month of birth and the deviations in the monthly birth distribution from the average monthly pattern is
0.93 (Pearson
correlation, one-sided test; P < 0.001) for adults
with high education, 0.82 (P < =0.001) for adults
with medium education, and 0.91 (P < 0.001) for adults with basic education. A similar result was found on the basis of a 10%
sample of the 1971 census of economically active, British-born males.
Non-manual workers tended to be born in spring and manual workers in
autumn and winter (22). In 1941, Goodenough showed that, in the higher
occupational classes, comparatively more births occur in spring
and summer (23).
|
Selective Survival in the First Year of Life or Debilitation in Utero or in Infancy. The third hypothesis assumes that selective survival during infancy is the causal mechanism that explains the relationship between month of birth and life expectancy. Specifically, the hypothesis is that autumn-born infants suffer higher mortality in their first year of life than spring-born infants. This selection would leave the relatively more robust individuals alive, who would experience lower mortality at adult ages. We used Danish data from the years 1911 to 1915 on seasonal infant mortality in the first year of life and found that, according to our model (Eq. 4), infants born in June are the most vulnerable. Compared with infants born in January, their standardized death rate during the first year of life is increased by 32% (Fig. 4). The model fits the data well with an adjusted R2 of 0.96. Studies of infant mortality by age and season of birth show a similar pattern for Switzerland (24) and Belgium (24, 25).
|
| |
Discussion and Conclusion |
|---|
|
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
|
|---|
Based on population data for Austria, Denmark, and Australia, we found that month of birth and remaining life expectancy at age 50 are related. We were able to show that neither the seasonal distribution of deaths nor social factors related to the seasonal distribution of births cause the differences in remaining life expectancy at age 50. We did not find support for the hypothesis that differential infant survival is the causal factor behind the observed phenomenon. Indeed, for Denmark, we found a significant positive correlation between the differences in infant mortality and the differences in mortality after age 50 by month of birth, which is consistent with the hypothesis that debilitation early in life is the causal mechanism.
Data that contain information about both perinatal and old-age conditions for the same individuals are extremely rare. Studies that use birth weight or other direct indicators of early life circumstances are mainly based on hospital data, which are inevitably subject to selection biases, and their sample sizes tend to be modest. We draw on the widely available information about the month of birth as a proxy for the severity of prenatal and early childhood conditions, which permits the use of complete and unselected population data of over a million observations.
The environment early in life affects the susceptibility of adults to infectious (27-29) as well as chronic diseases (4). These findings are congruent with our result that significant differences in mean age at death by month of birth exist for chronic diseases related to the cardiovascular, respiratory, and digestive systems, as well as for infectious diseases such as pneumonia and influenza. Significant differences exist for violent deaths, which at old age mainly consist of traffic accidents, accidental deaths other than traffic accidents, and suicides. At old age, accidental deaths and suicides are related to the health status of the individual, mainly to cardiovascular and other chronic diseases. Thus, it seems plausible that the increased susceptibility of the spring-born to chronic diseases also affects their overall risk to die from violent deaths.
Our findings are also consistent with ecological studies that found a significant positive correlation between arteriosclerotic heart disease and lung cancer at adult ages 40 to 69, and the infant mortality in the early years of the same cohorts (30, 31). These studies, which were published a quarter century ago, stimulated extensive research on how conditions early in life might influence health later in life. As reviewed below, much of this research focuses on birth weight or adult height.
Evidence from a large number of studies (for a critical review see ref. 32) suggests that fetal growth as reflected by birth weight is related to the occurrence of chronic diseases in adult life. For example, low birth weight for gestational age is associated with increased systolic blood pressure levels (4), serum cholesterol levels (33), abdominal obesity (4), and a decrease in lung function (34) at adult ages.
A study of weight at birth in Vienna, Austria, for infants born between 1865 and 1930 (35) shows that infants born between September and November have a significantly higher weight at birth (plus 47.3 g) than those born in the other months of the year. The author explains the higher birth weight by the better nutritional status of the mothers during pregnancy. This explanation is supported by the finding that birth weight differs less over the year in social groups that were less exposed to annual cycles in food commodities.
Seasonal differences in gestational age and weight at birth have also been attributed to the seasonal incidence of infectious diseases of the uro-genital tract of the mother during the third trimester of pregnancy (36, 37).
The relationship, however, between birth weight and adult susceptibility to diseases may be complex. Recent studies find a strong inverse relation between cardiovascular mortality of the mother and birth weight of her offspring, which suggests the existence of genetic and epigenetic intergenerational factors (38, 39). Intergenerational factors affecting the seasonal distribution of births are found in a Japanese study. The findings suggest that the birth month of the mother significantly influences the seasonal distribution of births of her offspring (40). In our study, the month of birth is therefore not merely a proxy for birth weight. It is a complex indicator for the nutritional status and the disease environment during the prenatal and early postnatal period of an infant, and for the intergenerational factors that may operate through birth weight. But not all effects of month of birth must necessarily operate through birth weight as the correlation in the seasonal distribution of births of mothers and their offspring shows.
A large number of studies show that adult height and adult mortality are negatively correlated and that height is primarily determined by genetic endowment and by nutritional status and disease environment early in life (41-43). A study of differential height at age 18 by month of birth for Austrian military recruits (44) reveals a significant difference of 6 mm between the tallest, who were born in April, and the shortest, who were born in October. This sinusoidal pattern is offset by half a year from the pattern found in remaining life expectancy at age 50. The study does not correct for the social status of the recruits, which is necessary because social status and height are closely related: as noted above, more Austrians with a high level of educational attainment are born in spring than in autumn. Furthermore, it may be that height is largely determined by genetic factors and by health and nutrition in childhood rather than in utero and infancy.
Our model of infant mortality does not permit any conclusions about whether debilitation occurred in utero or during early infancy, but the research on birth weight discussed above suggests that in utero conditions as determined by the health and the nutritional status of mothers may be particularly important. Individual level data that contain birth weight and lifespan by month of birth would certainly help to further clarify this question. We have already pointed out, however, that, at present, available data are scarce and suffer from selection biases.
Seasonal differences in nutrition and disease environment early in life could explain the relationship between month of birth and adult lifespan. In past decades, the food supplies in general, and the availability of fresh fruit and vegetables in particular, differed from season to season. Mothers who gave birth in autumn and early winter had access to plentiful food and fresh fruit and vegetables throughout most of their pregnancy; those who gave birth in spring and early summer experienced longer periods of inadequate nutrition.
It is important to point out that the mothers of the birth cohorts in our study were not exposed to severe seasonally occurring malnutrition. They rather suffered from seasonally inadequate nutrition. Over time, nutrition in winter and early spring has improved considerably, which is consistent with the result in our study that the relationship between month of birth and lifespan seems to be stronger among the older birth cohorts than among the more recently born.
About a quarter of the variation in human longevity may be due to genetic factors, a quarter to early-life factors, and the remaining half to adult and current living environments (1). The variance in adult lifespan by month of birth is small compared with the total variance and to the differences among social groups or between men and women. The finding, however, that these differences by month of birth are most probably linked to prenatal or early postnatal conditions related to nutrition or disease is of broad significance, with profound implications for clinical practice and public health policy. Through the increasing availability of large population-based vital data containing information about the entire lifespan, new research opportunities are opening up to further disentangle the complex mechanisms that link early and late life.
| |
Acknowledgements |
|---|
We thank K. Andreev, K. Brehmer, K. Christensen, L. Knudsen, J. Kytir, S. Leek, S. Pletcher, R. Rau, and A. Wienke.
| |
Abbreviation |
|---|
ICDN, international classification of diseases number.
| |
Footnotes |
|---|
* To whom reprint requests should be addressed. E-mail: doblhammer{at}demogr.mpg.de.
This paper was submitted directly (Track II) to the PNAS office.
| |
References |
|---|
|
Top
Abstract
Introduction
Data and Methods
Results
Discussion and Conclusion
References
|
|---|
| 1. | Vaupel, J. W. , Carey, J. R. , Christensen, K. , Johnson, T. E. , Yashin, A. I. , Holm, N. V. , Iachine, I. A. , Kannisto, V. , Khazaeli, A. A. , Liedo, P. , et al. (1998) Science 280, 855-860. |
| 2. | Christensen, K. & Vaupel, J. W. (1996) J. Intern. Med. 240, 333-341. |
| 3. | Fogel, R. W. & Costa, D. R. (1997) Demography 34, 49-66. |
| 4. | Barker, D. J. P. (1994) Mothers, Babies and Diseases in Later Life (BMJ Publishing Group, London). |
| 5. | Barker, D. J. P. (1995) Br. Med. J. 311, 171-174. |
| 6. | Kannisto, V. , Christensen, K. & Vaupel, J. W. (1997) Am. J. Epidemiol. 11, 987-994. |
| 7. | Christensen, K. , Vaupel, J. W. , Holm, N. V. & Yashin, A. I. (1995) Br. Med. J. 310, 432-436. |
| 8. | Greville, T. N. E. (1943) Rec. Am. Inst. Actuar. 32, 29-43. |
| 9. | Torrey, E. F. , Miller, J. , Rawlings, R. & Yolken, R. H. (1997) Schizophr. Res. 28, 1-38. |
| 10. | McGrath, J. , Welham, J. & Pemberton, M. (1995) Br. J. Psychiatry 167, 783-785. |
| 11. | Barak, Y. , Ring, A. , Sulkes, J. , Gabbay, U. & Elizur, A. (1995) Am. J. Psychiatry 152, 798-800. |
| 12. | Viedebech, P. & Nielsen, J. (1984) Hum. Genet. 65, 221-231. |
| 13. | Vézina, H. , Houde, L. , Charbonneau, H. , Beaudry, M. , Cholette, A. , Daoud, N. , Mathieu, J. , Robitaille, Y. , Veilleux, F. & Gauvreau, D. (1996) Psychol. Med. 26, 143-149. |
| 14. | Rothwell, P. M. , Gutnikov, S. A. , McKinney, P. A. , Schober, E. , Ionescu-Tigorviste, C. & Neu, A. (1999) Brit. Med. J. 319, 887-889. |
| 15. | Samuelsson, U. , Johansson, C. & Ludvigsson, J. (1999) Arch. Dis. Child 81, 143-146. |
| 16. | Jansson, B. & Malahy, M. A. (1981) Cancer Detect. Prev. 4, 291-294. |
| 17. | Yuen, J. , Ekbom, A. , Trichopoulos, D. , Hsieh, C. C. & Adami, H. O. (1994) Brit. J. Cancer 70, 564-568. |
| 18. | Aalberse, R. C. , Nieuwenhuys, E. J. , Hey, M. & Stapel, S. O. (1992) Exp. Allergy 22, 1003-1006. |
| 19. | Knodel, J. & Wilson, C. (1981) Popul. Stud. 35, 53-84. |
| 20. | Lam, D. A. & Miron, J. A. (1996) Demography 33, 291-306. |
| 21. | Anonymous. (1994) Jugend in Österreich: Fakten-Trends-Prognosen (Forschungsbericht 13 des Instituts für Demographie, Vienna). |
| 22. | Smithers, A. G. & Cooper, H. J. (1984) J. Soc. Psychol. 124, 79-84. |
| 23. | Goodenough, F. L. (1941) J. Genet. Psychol. 59, 65-76. |
| 24. | Breschi, M. & Bacci, M. L. (1996) in Infant and Child Mortality in the Past, eds. Desjardins, B. & Brignoli, P. (Clarendon, Oxford), pp. 157-173. |
| 25. | Huntington, E. (1938) Season of Birth (Wiley, New York). |
| 26. | Preston, S. H. & Haines, M. R. (1991) Fatal Years: Child Mortality in the Late Nineteenth-Century in America (Princeton Univ. Press, Princeton). |
| 27. | Moore, S. E. , Cole, T. J. , Poskitt, E. M. E. , Sonko, B. J. , Whitehead, R. G. , McGregor, I. A. & Prentice, A. M. (1997) Nature (London) 388, 434. |
| 28. | Chandra, K. R. (1974) Lancet 2, 1393-1394. |
| 29. | Winick, M. (1971) Pediatrics 47, 969-978. |
| 30. | Fohrsdahl, A. (1978) J. Epidemiol. Commun. H 32, 34-37. |
| 31. | Fohrsdahl, A. (1977) Br. J. Prev. Soc. Med. 31, 91-95. |
| 32. | Joseph, K. S. & Kramer, M. S. (1994) Epidemiol. Rev. 18, 158-174. |
| 33. | Fall, C. H. , Osmond, C. , Barker, D. J. , Clark, P. M. , Hales, C. N. , Stirling, Y. & Meade, T. W. (1995) Br. Med. J. 310, 428-432. |
| 34. | Barker, D. J. , Godfrey, K. M. , Fall, C. , Osmond, C. , Winter, P. D. & Shaheen, S. O. (1991) Br. Med. J. 303, 671-675. |
| 35. | Ward, W. P. (1987) Ann. Hum. Biol. 14, 495-506. |
| 36. | Rousham, E. K. & Gracey, M. (1998) Aust. N. Z. J. Public Health 22, 669-672. |
| 37. | Keller, C. A. & Nugent, R. P. (1983) Am. J. Epidemiol. 118, 689-698. |
| 38. | Davey Smith, G. , Harding, S. & Rosato, M. (2000) Br. Med. J. 320, 839-840. |
| 39. | Davey Smith, G. , Hart, C. , Ferrell, C. , Upton, M. , Hole, D. , Hawthorne, V. & Watt, G. (1997) Br. Med. J. 315, 1189-1193. |
| 40. | Miura, T. , Shimura, M. , Nakamura, I. & Nonaka, K. (1987) in Seasonality of Birth, ed. Miura, T. (SPB Academic Publishing, The Hague, The Netherlands), pp. 45-55. |
| 41. | Waaler, H. F. (1984) Acta Med. Scand. Suppl. 679, 1-56. |
| 42. | Floud, R. , Wachter, K. & Gregory, A. (1990) Height, Health and History (Cambridge Univ. Press, Cambridge, U.K.). |
| 43. | Fogel, R. W. (1994) Am. Econ. Rev. 84, 369-395. |
| 44. | Weber, G. , Prossinger, H. & Seidler, H. (1998) Nature (London) 391, 754-755. |
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg What's this?
This article has been cited by other articles in HighWire Press-hosted journals:
|
|
 |
|
  S. Huber, R. Didham, and M. Fieder Month of birth and offspring count of women: data from the Southern hemisphere Hum. Reprod., May 1, 2008; 23(5): 1187 - 1192. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Jongbloet Month of birth in relation to suicide The British Journal of Psychiatry, April 1, 2008; 192(4): 313 - 313. [Full Text] [PDF]
|
|
|
|
 |
|
 D. L. Costa, L. A. Helmchen, and S. Wilson Economics of Health and Mortality Special Feature: Race, infection, and arteriosclerosis in the past PNAS, August 14, 2007; 104(33): 13219 - 13224. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. M. Cutler, G. Miller, and D. M. Norton Economics of Health and Mortality Special Feature: Evidence on early-life income and late-life health from America's Dust Bowl era PNAS, August 14, 2007; 104(33): 13244 - 13249. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Lummaa, J. E. Pettay, and A. F. Russell Male twins reduce fitness of female co-twins in humans PNAS, June 26, 2007; 104(26): 10915 - 10920. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. H. JONGBLOET Do sunlight and vitamin D reduce the likelihood of colon cancer? Time for a paradigm shift? Int. J. Epidemiol., October 1, 2006; 35(5): 1359 - 1360. [Full Text] [PDF]
|
|
|
|
|
|
E. SALIB and M. CORTINA-BORJA Effect of month of birth on the risk of suicide The British Journal of Psychiatry, May 1, 2006; 188(5): 416 - 422. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Catalano and T. Bruckner Secondary sex ratios and male lifespan: Damaged or culled cohorts PNAS, January 31, 2006; 103(5): 1639 - 1643. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Cagnacci, F. S. Pansini, A. Bacchi-Modena, N. Giulini, G. Mollica, D. De Aloysio, E. Vadora, A. Volpe, and for the Emilia-Romagna Operative Group for the Men Season of birth influences the timing of menopause Hum. Reprod., August 1, 2005; 20(8): 2190 - 2193. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. D. Gluckman and M. A. Hanson Living with the Past: Evolution, Development, and Patterns of Disease Science, September 17, 2004; 305(5691): 1733 - 1736. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. E. Finch and E. M. Crimmins Inflammatory Exposure and Historical Changes in Human Life-Spans Science, September 17, 2004; 305(5691): 1736 - 1739. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D A Lawlor, G Davey Smith, R Mitchell, and S Ebrahim Temperature at birth, coronary heart disease, and insulin resistance: cross sectional analyses of the British women's heart and health study Heart, April 1, 2004; 90(4): 381 - 388. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Huber, M. Fieder, B. Wallner, K. Iber, and G. Moser Effects of season of birth on reproduction in contemporary humans: Brief communication Hum. Reprod., February 1, 2004; 19(2): 445 - 447. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Lawlor Commentary: The art and science of epidemiology: governed by the seasons? Int. J. Epidemiol., February 1, 2004; 33(1): 144 - 146. [Full Text] [PDF]
|
|
|
|
|
|
P.H. Jongbloet The male disadvantage and the seasonal rhythm of sex ratio at the time of conception Hum. Reprod., November 1, 2003; 18(11): 2491 - 2492. [Full Text] [PDF]
|
|
|
|
 |
|
 L. A. Gavrilov and N. S. Gavrilova The Quest for a General Theory of Aging and Longevity Sci. Aging Knowl. Environ., July 16, 2003; 2003(28): re5 - 5. |
