Contemporary methods of mortality analysis: Determinants of Exceptional Longevity

1. Contemporary methods of mortality analysis:Determinants of Exceptional 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. Approach To study “success stories” in long-term avoidance of fatal diseases (survival to 100 years) and factors correlated with this remarkable survival success

3. 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 to explore early-childhood predictors of exceptional longevity Jeanne Calment (1875-1997)

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

5. 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

6. 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

7. 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

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. Results of Centenarian Age Verification

11. 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

12. Predictors of Exceptional Longevity

13. Study 1:Compare centenarians with their siblings (within-family study)

14. Within-Family Study of Exceptional Longevity Cases - 198 Centenarians born in U.S. in 1890-1893 Controls – Their own siblings Method: Conditional logistic regression Advantage: Allows researchers to eliminate confounding effects of between-family variation

15. Design of the Study

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

17. First-born siblings are more likely to become centenarians (odds = 1.8)

18. Birth Order and Odds to Become a Centenarian

19. 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)

20. First-born adult siblings (20+years) are more likely to become centenarians (odds = 1.95)

21. Are young fathers responsible for birth order effect?

22. Birth order is more important than paternal age for chances to become a centenarian

23. Are young mothers responsible for the birth order effect?

24. Maternal Age at Person’s Birth and Odds to Become a Centenarian

25. Birth order effect explained:Being born to young mother!

26. Even at age 75 it still helps to be born to young mother (age <25)(odds = 1.9)

27. Question • Families were quite large in the past, particularly those covered by genealogical records (large family size bias). • Is the "young mother effect" robust to the family size, and is it observed in smaller families too? • Or is it confined to extremely large families only? Approach: To split data in two equal parts by median family size (9 children) and re-analyze the data in each group separately.

28. Results • In smaller families (less than 9 children) the effect of young mother is even larger: Odds ratio = 2.23, P=0.004; 95%CI = 1.30 - 3.98 • Compare to larger families (more than 9 children): Odds ratio = 1.72, P=0.11; 95%CI = 0.88 - 3.34 Conclusion: "Young mother effect" is not confined to extremely large family size

29. Being born to Young Mother Helps Laboratory Mice to Live Longer • Source: Tarin et al., Delayed Motherhood Decreases Life Expectancy of Mouse Offspring. Biology of Reproduction 2005 72: 1336-1343.

30. Possible explanation These findings are consistent with the 'best eggs are used first' hypothesis suggesting that earlier formed oocytes are of better quality, and go to fertilization cycles earlier in maternal life.

31. Study 2:Compare centenarians when they were young adults to their peersusing WWI Civil Draft Registration Cards

32. Physical Characteristics at Young Age and Survival to 100 A study of height and build of centenarians when they were young using WWI civil draft registration cards

33. Height – What to Expect Height seems to be a good indicator of nutritional status and infectious disease history in the past. Historical studies showed a negative correlation between height and mortality. Hence we may expect that centenarians were taller than average

34. Build – What to Expect Slender build may suggest a poor nutrition during childhood. We may expect that centenarians were less likely to be slender when young. On the other hand, biological studies suggest that rapid growth may be harmful and somewhat delayed maturation may be beneficial for longevity.

35. Small Dogs Live Longer Miller RA. Kleemeier Award Lecture: Are there genes for aging? J Gerontol Biol Sci 54A:B297–B307, 1999.

36. Small Mice Live Longer Source: Miller et al., 2000. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 55:B455-B461

37. Data Sources Social Security Administration Death Master File WWI civil draft registration cards (completed for almost 100 percent men born between 1873 and 1900)

38. Study Design Cases: men centenarians born in 1887 (randomly selected from the SSA Death Master File) and linked to the WWI civil draft records. Out of 120 selected men, 19 were not eligible for draft. The linkage success for remaining 101 records was 75% (76 records) Controls: men matched on birth year, race and county of WWI civil draft registration

39. Design of the Study

40. WWI Civilian Draft Registration In 1917 and 1918, approximately 24 million men born between 1873 and 1900 completed draft registration cards. President Wilson proposed the American draft and characterized it as necessary to make "shirkers" play their part in the war. This argument won over key swing votes in Congress.

41. WWI Draft Registration Registration was done in three parts, each designed to form a pool of men for three different military draft lotteries. During each registration, church bells, horns, or other noise makers sounded to signal the 7:00 or 7:30 opening of registration, while businesses, schools, and saloons closed to accommodate the event.

42. Registration Day Parade

44. Information Available in the Draft Registration Card age, date of birth, race, citizenship permanent home address occupation, employer's name height (3 categories), build (3 categories), eye color, hair color, disability

46. SAMPLE CHARACTERISTICS (%)

47. BODY HEIGHT AND BODY BUILD DISTRIBUTIONS (%)

48. Height Distribution Among Centenarians and Controls

49. Body Build Distribution Among Centenarians and Controls

50. Multivariate Analysis Conditional multiple logistic regression model for matched case-control studies to investigate the relationship between an outcome of being a case (extreme longevity) and a set of prognostic factors (height, build, occupation, marital status, number of children, immigration status) Statistical package Stata-10, command clogit