Predictors of Exceptional Human Longevity

1. Predictors of Exceptional Human Longevity Dr. Leonid A. Gavrilov, Ph.D. Dr. Natalia S. Gavrilova, Ph.D. Center on Aging NORC and The University of Chicago Chicago, Illinois, USA

2. Centenarians represent the fastest growing age group in the industrialized countries Yet, factors predicting exceptional longevity and its time trends remain to be fully understood In this study we explored the new opportunities provided by the ongoing revolution in information technology, computer science and Internet expansion Jeanne Calment (1875-1997)

3. Revolution in Information TechnologyWhat does it mean for longevity studies? Over 75 millions of computerized genealogical records are available onlinenow!

4. Computerized genealogies is a promising source of information about potential predictors of exceptional longevity: life-course events, early-life conditions and family history of longevity

5. Computerized Genealogies as a Resource for Longevity Studies • Pros: provide important information about family and life-course events, which otherwise is difficult to collect (including information about lifespan of parents and other relatives) • Cons: Uncertain data quality Uncertain validity and generalizability

6. For longevity studies the genealogies with detailed birth dates and death dates for long-lived individuals (centenarians) and their relatives are of particular interest In this study 1,001 genealogy records for centenarians born in 1875-1899 were collected and used for further age validation

8. Internet Resources Used in Centenarian Age Verification Social Security Administration Death Master File is publicly available at the Rootsweb website: http://ssdi.rootsweb.com/cgi-bin/ssdi.cgi Head of household indexes and census page images for 1900, 1920 and 1910 federal censuses are provided by Genealogy.com Individual indexes of enumerated persons by 1900, 1920 and 1930 federal censuses and census page images are provided by Ancestry.com

9. Steps of Centenarian Age Verification • Internal consistency checks of dates • Verification of death dates – linkage to the Social Security Administration Death Master File (DMF) • Verification of birth dates – linkage to early Federal censuses (1900, 1910, 1920, 1930)

10. A typical image of ‘centenarian’ family in 1900 census

11. Results of Centenarian Age Verification

12. Conclusions of the Age Verification Study • Death dates of centenarians recorded in genealogies always require verification because of strong outliers (1.3%, misprints) • Birth dates of centenarians recorded in genealogies are sufficiently accurate - 92% are correct; for the remaining 8% only one-year disagreements • Quality of genealogical data is good enough if these data are pre-selected for high data quality

13. Birth Order and Chances to Become a Centenarian Cases - 436 centenarians born in the United States between 1890 and 1899 Controls – their siblings born in the same time window (1,119 controls) Model: log(longevity odds ratio) = ax + bx2 + cz + d where x – birth order; z – family size; a,b,c,d – parameters of polynomial regression model

14. Birth Order and Survival to 100 Source: Gavrilova, N.S., Gavrilov, L.A. Search for Predictors of Exceptional Human Longevity. In: “Living to 100 and Beyond” Monograph. The Society of Actuaries, Schaumburg, Illinois, USA, 2005, pp. 1-49.

15. New Developments Can birth order effect be confirmed by more rigorous approach – a strictly within-family analysis? Method of conditional logistic regression allows us to compare centenarians with their siblings within the same family. This eliminates confounding caused by between-family variation.

16. First-born siblings are more likely to become centenarians (odds = 1.8) Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(2) = 33.75 Prob > chi2 = 0.0000 Log likelihood = -282.22348 Pseudo R2 = 0.0564 ---------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------- First-born status1.7720.006 1.180 2.663 Male sex .404 0.000 .284 .576 ---------------------------------------------------------------------------------

17. Can the birth-order effect be a result of selective child mortality, thus not applicable to adults? Approach: • To compare centenarians with those siblings only who survived to adulthood (age 20)

18. First-born adult siblings (20+years) are more likely to become centenarians (odds = 1.95) Conditional (fixed-effects) logistic regression Number of obs = 797 LR chi2(2) = 27.54 Prob > chi2 = 0.0000 Log likelihood = -247.93753 Pseudo R2 = 0.0526 ---------------------------------------------------------------------------------- Variable | Odds Ratio P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------------- First-born status | 1.9490.003 1.261 3.010 Male sex | .458 0.000 .318 .658 ----------------------------------------------------------------------------------

19. Even at age 75 it still helps to be a first-born child (odds = 1.7) Conditional (fixed-effects) logistic regression Number of obs = 557 LR chi2(2) = 19.03 Prob > chi2 = 0.0001 Log likelihood = -186.22869 Pseudo R2 = 0.0486 ---------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+-------------------------------------------------- First-born status1.6590.040 1.022 2.693 Male sex .459 0.000 .306 .687 ----------------------------------------------------------------

20. Are young fathers responsible for birth order effect? Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(2) = 30.11 Prob > chi2 = 0.0000 Log likelihood = -284.04284 Pseudo R2 = 0.0503 --------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+------------------------------------------------------------- Born to young father1.8560.056 .985 3.496 Male sex .415 0.000 .291 .590 ---------------------------------------------------------------------------

21. Birth order is more important than paternal age for chances to become a centenarian Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(3) = 34.24 Prob > chi2 = 0.0000 Log likelihood = -281.97993 Pseudo R2 = 0.0572 ---------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+-------------------------------------------------- First-born status 1.635 0.039 1.025 2.607 Born to young father 1.294 0.484 .628 2.668 Male sex .407 0.000 .285 .580 --------------------------------------------------------------------------------------

22. Are young mothers responsible for the birth order effect? Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(2) = 37.35 Prob > chi2 = 0.0000 Log likelihood = -280.42473 Pseudo R2 = 0.0624 ------------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------------------- Born to young mother2.0310.001 1.326 3.110 Male sex .412 0.000 .289 .586 -------------------------------------------------------------------------------------

23. Birth order effect explained:Being born to young mother! Conditional (fixed-effects) logistic regression Number of obs = 950 LR chi2(3) = 39.05 Prob > chi2 = 0.0000 Log likelihood = -279.57165 Pseudo R2 = 0.0653 ------------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------------------- First-born status 1.360 0.189 .859 2.153 Born to young mother1.7600.021 1.089 2.846 Male sex .407 0.000 .285 .580 --------------------------------------------------------------------------------------

24. Even at age 75 it still helps to be born to young mother (age <25)(odds = 1.9) Conditional (fixed-effects) logistic regression Number of obs = 557 LR chi2(2) = 21.31 Prob > chi2 = 0.0000 Log likelihood = -185.08639 Pseudo R2 = 0.0544 ---------------------------------------------------------------------------------- Variable Odds Ratio P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------------- Born to young mother1.8690.012 1.145 3.051 Male sex .461 0.000 .307 .690 ----------------------------------------------------------------------------------

25. Back to a broader comparison of ‘centenarian’ and ‘non-centenarian’ families

26. Case-Control Study of Early-Life Conditions and Exceptional Longevity Cases - 382 households where centenarians (born in 1890-1899) were raised (from centenarian records linked to 1900 census) Controls – 1% random sample of households with children below age 10enumerated by 1900 census (from Integrated Public Use Microdata Sample, IPUMS: http://www.ipums.umn.edu/usa/index.html)

27. Childhood Residence and Survival to Age 100Odds for household to be in a ‘centenarian’ group A – New England and Middle Atlantic (reference group) B – Mountain West and Pacific West C – Southeast and Southwest D – North Central

28. Household Property Status During Childhood and Survival to Age 100Odds for household to be in a ‘centenarian’ group A – Rented House B – Owned House C – Rented Farm D – Owned farm (reference group)

29. Paternal Immigration Status and Survival to Age 100Odds for household to be in a ‘centenarian’ group A – Father immigrated B – Father native-born (reference group)

30. No Association was Found (so far) Between Chances to Become a Centenarian and • Paternal literacy • Child mortality of siblings

31. Month of Birth Predicts the US Life Expectancy at Age 80 Computed using the Social Security Administration data Source: Gavrilova, N.S., Gavrilov, L.A. Search for Predictors of Exceptional Human Longevity. In: “Living to 100 and Beyond” Monograph. The Society of Actuaries, Schaumburg, Illinois, USA, 2005, pp. 1-49.

32. Seasonality (month-of-birth effects) for US life expectancy

33. Within-Family Study of Month-of-Birth Effects on Exceptional Longevity Cases - Centenarians born in 1890-1893 Controls – Their own siblings Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between-family variation

34. Month of Birth and the Likelihood to Become a Centenarian Method: Conditional logistic regression for odds to become a centenarian, using siblings as within-family control. 921 observations

35. Month of Birth and the Likelihood to Become a Centenarian for Adult Siblings (20+ years) Method: Conditional logistic regression for odds to become a centenarian, using siblings as within-family control. 787 observations

36. Conclusions • The shortest conclusion was suggested in the title of the New York Times article about this study

38. Conclusions The accuracy of 'longevity risk' estimates can be greatly improved by using such 'trivial' information about person's childhood as: • Mother's age at person's birth • Month of birth • Place of birth and some other characteristics of parental family Most important, these findings indicate that a subsequent large-scale research project on early-life determinants of human longevity is likely to produce more new results, very important for actuarial science and practice

39. Acknowledgments This study was made possible thanks to: generous support from the Society of Actuaries and the National Institute on Aging

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