Predictors of Exceptional Longevity Dr. Leonid A. Gavrilov, Ph.D. Dr. Natalia S. Gavrilova, Ph.D....

Predictors 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

What Do We Know About Mortality of

Centenarians?

A Study That Answered This Question

M. Greenwood, J. O. Irwin. BIOSTATISTICS OF SENILITY

Mortality at Advanced Ages

Source: Gavrilov L.A., Gavrilova N.S. The Biology of Life Span:

A Quantitative Approach, NY: Harwood Academic Publisher, 1991

Mortality Deceleration in Other Species

Invertebrates: Nematodes, shrimps,

bdelloid rotifers, degenerate medusae (Economos, 1979)

Drosophila melanogaster (Economos, 1979; Curtsinger et al., 1992)

Medfly (Carey et al., 1992) Housefly, blowfly

(Gavrilov, 1980) Fruit flies, parasitoid wasp

(Vaupel et al., 1998) Bruchid beetle (Tatar et

al., 1993)

Mammals: Mice (Lindop, 1961;

Sacher, 1966; Economos, 1979)

Rats (Sacher, 1966) Horse, Sheep, Guinea

pig (Economos, 1979; 1980)

However no mortality deceleration is reported for

Rodents (Austad, 2001) Baboons (Bronikowski

et al., 2002)

Mortality Leveling-Off in House Fly

Musca domestica

Our analysis of the life table for 4,650 male house flies published by Rockstein & Lieberman, 1959.

Source: Gavrilov & Gavrilova.

Handbook of the Biology of Aging, Academic Press, 2006, pp.3-42.

Age, days

0 10 20 30 40

ha

zard

ra

te,

log

sc

ale

0.001

0.01

0.1

Existing Explanations of Mortality Deceleration

Population Heterogeneity (Beard, 1959; Sacher, 1966). “… sub-populations with the higher injury levels die out more rapidly, resulting in progressive selection for vigour in the surviving populations” (Sacher, 1966)

Exhaustion of organism’s redundancy (reserves) at extremely old ages so that every random hit results in death (Gavrilov, Gavrilova, 1991; 2001)

Lower risks of death for older people due to less risky behavior (Greenwood, Irwin, 1939)

Evolutionary explanations (Mueller, Rose, 1996; Charlesworth, 2001)

Problems in Hazard Rate Estimation

At Extremely Old Ages Mortality deceleration in humans

may be an artifact of mixing different birth cohorts with different mortality (heterogeneity effect)

Standard assumptions of hazard rate estimates may be invalid when risk of death is extremely high

Ages of very old people may be highly exaggerated

Social Security Administration Death Master File Helps to

Alleviate the First Two Problems Allows to study mortality in large,

more homogeneous single-year or even single-month birth cohorts

Allows to study mortality in one-month age intervals narrowing interval of hazard rates estimation

What Is SSA DMF ?

SSA DMF is a publicly available data resource (available at Rootsweb.com)

Covers 93-96 percent deaths of persons 65+ occurred in the United States in the period 1937-2009

Some birth cohorts covered by DMF could be studied by method of extinct generations

Considered superior in data quality compared to vital statistics records by some researchers

Social Security Administration Death Master File (DMF) Was Used in This Study:

(1) Study of cohort mortality at advanced ages: Estimation of hazard rates for each month of age for single-year extinct birth cohorts.

(2) Month-of-birth and mortality after age 80: Estimation of life expectancy in real birth cohort according to month of birth.

Hypothesis

Mortality deceleration at advanced ages should be less expressed for data of higher quality

Quality Control (1)

Study of mortality in states with different quality of age reporting:

Records for persons applied to SSN in the Southern states, Hawaii and Puerto Rico were suggested to have lower quality (Rosenwaike, Stone, 2003)

Mortality when all data are used

Mortality for data with presumably different quality

Quality Control (2)

Study of mortality for earlier and later single-year extinct birth cohorts:

Records for later born persons were suggested to have higher quality due to more accurate age reporting.

Mortality for data with presumably different quality

Mortality at Advanced Ages by Sex

Mortality at Advanced Ages by Sex

Mortality at Advanced Ages by Sex

Mortality at Advanced Ages by Sex

Mortality at Advanced Ages by Sex

Mortality at Advanced Ages by Sex

Mortality at advanced ages: Actuarial 1900 cohort life table

and SSDI 1887 cohort

Age

60 70 80 90 100 110

log

(ha

zard

ra

te)

-2

-1

01900 cohort, U.S. life table1887 cohort from SSDI

Source for actuarial life table:Bell, F.C., Miller, M.L.Life Tables for the United States Social Security Area 1900-2100Actuarial Study No. 116

Mortality at advanced ages: Actuarial cohort life table

and SSDI 1887 cohortEstimating Gompertz slope parameter

Age

80 85 90 95 100 105 110

log

(ha

za

rd r

ate

)

-1

0

1900 cohort, U.S. life table1887 cohort from SSDI

1900 cohort, age interval 50-100 alpha (95% CI):0.092 (0.092,0.093)

1887 cohort, age interval 85-103 alpha (95% CI):0.094 (0.093,0.095)

1887 cohort, age interval 85-100 alpha (95% CI):0.117 (0.116,0.118)

Female/Male ratio after age 1001886 birth cohort

Age

100 102 104 106 108 110

Fe

ma

le/M

ale

Ra

tio

3

4

5

6

7

8

SSDI Data Quality Evaluation

Month-of-Birth and Mortality at Advanced Ages

SSA Death Master File allows researchers to study mortality in real birth cohorts by month-of-birth

Provides more accurate and unbiased estimates of life expectancy by month of birth compared to usage of cross-sectional death certificates

Month of Birth

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

life

exp

ecta

ncy a

t ag

e 8

0,

years

7.6

7.7

7.8

7.9

1885 Birth Cohort1891 Birth Cohort

Month-of-Birth effects disappear at age 100+

Conclusions Late-life mortality deceleration

appears to be not that strong - cohort mortality at advanced ages continues to grow up to age 105 years

Month of birth effects on mortality exist at age 80 but then fade and disappear by age 100+

Predictors of Exceptional Longevity

Data Sources

1. Social Security Administration Death Master File

2. WWI civil draft registration cards (completed for almost 100 percent men born between 1873 and 1900)

Design of the Study

De athw in d o w fo r

ce n te n arian s

Birthyear

Death window for controls

Controls: M ales m atched on birth year, race andcounty of registration

M ale Centenarians born in 1887(from the Social Security Adm inistration Death M aster F ile)

1887 1917-18 1987

W W I draftregistration

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.

WWI Draft RegistrationRegistration 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.

Registration Day Parade

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

Draft Registration Card:An Example

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 240 selected men, 15 were not eligible for draft. The linkage success for remaining records was 77.5% (174 records)

Controls: men matched on birth year, race and county of WWI civil draft registration

SAMPLE CHARACTERISTICS (%)

Centenarians

Controls

Foreign born

20.5 22.2

Married 68.4 63.7

Had children 52.6 42.1

Farmers 31.6 23.4

African Am. 5.3 5.3

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

Height – What to Expect

1. Height seems to be a good indicator of nutritional status and infectious disease history in the past.

2. Historical studies showed a negative correlation between height and mortality.

3. Hence we may expect that centenarians were taller than average

Build – What to Expect

1. Slender build may suggest a poor nutrition during childhood. We may expect that centenarians were less likely to be slender when young.

2. On the other hand, biological studies suggest that rapid growth may be harmful and somewhat delayed maturation may be beneficial for longevity.

Small Dogs Live Longer

Small Mice Live Longer

171 pairs of men born in 1887

Centenarians Controls

Body Height short 6.4 8.8 medium 65.5 56.7 tall 28.1 34.5Body Build slender 25.2 25.2 medium 67.8 60.2 stout 7.0 14.6

BODY HEIGHT AND BODY BUILD DISTRIBUTIONS (%)

Height Distribution Among Centenarians and Controls

Body Build Distribution Among Centenarians and

Controls

Results of multivariate study

Variable OR P-value

Medium height vs short and tall height

1.35 0.260

Slender and medium build vs stout build

2.63* 0.025

Farming 2.20* 0.016

Married vs unmarried 0.68 0.268

Native born vs foreign b.

1.13 0.682

Results of multivariate study

Significant predictors only

Variable Odds Ratio

P-value

‘Slender’ body build reference: stout build

2.54 0.040

‘Medium’ body build reference: stout build

2.64 0.017

Farming 1.99 0.025

Household Property Status During Childhood (1900 census) and Survival to

Age 100

Odds for household to be in a ‘centenarian’ group

A – Rented House (reference group)

B – Owned House

C – Rented Farm

D – Owned farm0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

A B C D

MalesFemales

Source: Gavrilova, Gavrilov, NAAJ, 2007

Other physical characteristics

Variable Odds Ratio

P-value

Blue eye color 1.62 0.069

‘Short’ body height reference: tall height

1.02 0.967

‘Medium’ body height reference: tall height

1.43 0.212

Other variables include body build and farming

Having children by age 30 and survival to age 100

Conditional (fixed-effects) logistic regressionN=171. Reference level: no children

VariableOdds ratio

95% CIP-

value

1-3 children 1.620.89-2.95

0.127

4+ children 2.710.99-7.39

0.051

Conclusion

The study of height and body build among men born in 1887 suggests that obesity at young adult age (30 years) has strong long-lasting effect in preventing longevity

Other Conclusions

Both farming and having large number of children (4+) at age 30 significantly increased the chances of exceptional longevity by 100-200%. The effects of immigration status, marital status, and body height on longevity were less important, and they were statistically insignificant in the studied data set.

Acknowledgments

This study was made possible thanks to:

generous support from the

National Institute on Aging The Society of Actuaries grant Stimulating working environment at the Center on Aging, NORC/University of Chicago

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