Transcript Predictors of Exceptional Human Longevity

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
2006 Chicago Actuarial Association Workshop
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)
2006 Chicago Actuarial Association Workshop
Revolution in Information
Technology
What does it mean for longevity
studies?
Over 75 millions of
computerized
genealogical records are
available online now!
2006 Chicago Actuarial Association Workshop
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
2006 Chicago Actuarial Association Workshop
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
2006 Chicago Actuarial Association Workshop
For longevity studies the genealogies with
detailed birth dates and death dates for longlived 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
2006 Chicago Actuarial Association Workshop
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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
2006 Chicago Actuarial Association Workshop
Steps of Centenarian Age
Verification
1.
2.
3.
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)
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A typical image of ‘centenarian’
family in 1900 census
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Results of Centenarian
Age Verification
1001 records
990 records used for further
consistency checks verification
990 records were
linked to the SSA
Death Master File
Linkage success rate 77% (80% for
centenarians born after 1890)
In 3% of cases centenarian status
was not confirmed
548 records found
Linkage success rate 80% when
in DMF for persons using Genealogy.com and 91% after
born in 1890-1899 supplementation with Ancestry.com.
were then linked to In 8% of cases a 1-year
early US censuses disagreement between genealogy
and census record was observed
2006 Chicago Actuarial Association Workshop
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
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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
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Birth Order and Survival to 100
0.8
Odds to become a centenarian
0.7
Females
Males
Source:
0.6
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.
0.5
0.4
0.3
0.2
0.1
1
2
3
4
5
6
7
8
9
10
Birth order
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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 betweenfamily variation.
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First-born siblings are more likely
to become centenarians
(odds = 1.8)
Conditional (fixed-effects) logistic regression
Log likelihood = -282.22348
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
950
33.75
0.0000
0.0564
---------------------------------------------------------------------Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------------
First-born status
1.772
0.006
1.180
2.663
Male sex
.404
0.000
.284
.576
---------------------------------------------------------------------------------
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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)
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First-born adult siblings
(20+years) are more likely to
become centenarians
(odds = 1.95)
Conditional (fixed-effects) logistic regression
Log likelihood = -247.93753
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
797
27.54
0.0000
0.0526
---------------------------------------------------------------------------------Variable
| Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------------------------
First-born status |
1.949
0.003
1.261
3.010
Male sex |
.458
0.000
.318
.658
----------------------------------------------------------------------------------
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Even at age 75 it still helps
to be a first-born child
(odds = 1.7)
Conditional (fixed-effects) logistic regression
Log likelihood = -186.22869
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
557
19.03
0.0001
0.0486
---------------------------------------------------------------Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------
First-born status
1.659
0.040
1.022
2.693
Male sex
.459
0.000
.306
.687
----------------------------------------------------------------
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Are young fathers responsible for
birth order effect?
Conditional (fixed-effects) logistic regression
Log likelihood = -284.04284
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
950
30.11
0.0000
0.0503
--------------------------------------------------------------------------Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+-------------------------------------------------------------
Born to young father
Male sex
1.856
0.056
.985
3.496
.415
0.000
.291
.590
---------------------------------------------------------------------------
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Birth order is more important than
paternal age for chances to become
a centenarian
Conditional (fixed-effects) logistic regression
Log likelihood = -281.97993
Number of obs
LR chi2(3)
Prob > chi2
Pseudo R2
=
=
=
=
950
34.24
0.0000
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
--------------------------------------------------------------------------------------
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Are young mothers responsible for
the birth order effect?
Conditional (fixed-effects) logistic regression
Log likelihood = -280.42473
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
950
37.35
0.0000
0.0624
------------------------------------------------------------------------------------Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+-----------------------------------------------------------------------
Born to young mother
2.031
0.001
1.326
3.110
Male sex
.412
0.000
.289
.586
-------------------------------------------------------------------------------------
2006 Chicago Actuarial Association Workshop
Birth order effect explained:
Being born to young mother!
Conditional (fixed-effects) logistic regression
Log likelihood = -279.57165
Number of obs
LR chi2(3)
Prob > chi2
Pseudo R2
=
=
=
=
950
39.05
0.0000
0.0653
-------------------------------------------------------------------------------------
Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+-----------------------------------------------------------------------
First-born status
1.360
0.189
.859
2.153
Born to young mother
1.760
0.021
1.089
2.846
Male sex
.407
0.000
.285
.580
--------------------------------------------------------------------------------------
2006 Chicago Actuarial Association Workshop
Even at age 75 it still helps to be
born to young mother (age <25)
(odds = 1.9)
Conditional (fixed-effects) logistic regression
Log likelihood = -185.08639
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
557
21.31
0.0000
0.0544
----------------------------------------------------------------------------------
Variable
Odds Ratio
P>|z|
[95% Conf. Interval]
-------------+--------------------------------------------------------------------
Born to young mother
1.869
0.012
1.145
3.051
Male sex
.461
0.000
.307
.690
----------------------------------------------------------------------------------
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Back to a broader comparison
of ‘centenarian’ and
‘non-centenarian’ families
2006 Chicago Actuarial Association Workshop
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 10
enumerated by 1900 census (from
Integrated Public Use Microdata Sample,
IPUMS: http://www.ipums.umn.edu/usa/index.html)
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Childhood Residence
and Survival to Age 100
Odds for household to be in a ‘centenarian’ group
A – New England
and Middle
Atlantic
(reference
group)
3.5
3
2.5
Males
Females
2
B – Mountain West
and Pacific West
1.5
1
C – Southeast and
Southwest
0.5
0
A
B
C
D
D – North Central
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Household Property Status During Childhood
and Survival to Age 100
Odds for household to be in a ‘centenarian’ group
Males
Females
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
A – Rented House
B – Owned House
C – Rented Farm
D – Owned farm
(reference group)
A
B
C
D
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Paternal Immigration Status
and Survival to Age 100
Odds for household to be in a ‘centenarian’ group
Males
Females
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
A – Father
immigrated
B – Father
native-born
(reference
group)
A
B
2006 Chicago Actuarial Association Workshop
No Association was Found (so far)
Between Chances to Become a
Centenarian and


Paternal literacy
Child mortality of siblings
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Month of Birth Predicts the US Life
Expectancy at Age 80
Computed using the Social Security Administration data
life expectancy at age 80, years
7.9
1885 Birth Cohort
1891 Birth Cohort
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.
7.8
7.7
7.6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month of Birth
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Seasonality (month-of-birth effects)
for US life expectancy
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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 betweenfamily variation
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Month of Birth and the Likelihood
to Become a Centenarian
Method:
Conditional logistic
regression for
odds to become a
centenarian, using
siblings as withinfamily control.
921 observations
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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 withinfamily control.
787 observations
2006 Chicago Actuarial Association Workshop
Conclusions

The shortest conclusion was
suggested in the title of the
New York Times article about
this study
2006 Chicago Actuarial Association Workshop
2006 Chicago Actuarial Association Workshop
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
2006 Chicago Actuarial Association Workshop
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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For More Information and Updates
Please Visit Our
Scientific and Educational Website
on Human Longevity:
 http://longevity-science.org
And Please Post Your Comments at
our Scientific Discussion Blog:

http://longevity-science.blogspot.com/
2006 Chicago Actuarial Association Workshop