Friday, February 20, 2015

Mortality Trajectories at Extreme Old Ages

Dear Colleagues,

We are pleased to alert you that our new peer-reviewed study on Mortality Trajectories at Extreme Old Ages is now publicly available at PubMed Central® (PMC) - a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318539/

Comments and suggestions are most welcome!

Kind regards,

-- Leonid and Natalia

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-- Leonid Gavrilov, Ph.D., GSA Fellow
-- Natalia Gavrilova, Ph.D., GSA Fellow
Center on Aging, NORC at the University of Chicago
Website: http://longevity-science.org/

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Mortality Trajectories at Extreme Old Ages:
A Comparative Study of Different Data Sources on U.S. Old-Age Mortality

Gavrilova N.S., Gavrilov L.A.
In: 2014 Living to 100 Monograph
[available in PMC 2015 Feb 5 ].
The Society of Actuaries, 2014, 23 pages, PMID: 25664347

Abstract:

The growing number of individuals living beyond age 80 underscores the need for accurate measurement of mortality at advanced ages. Our earlier published study challenged the common view that the exponential growth of mortality with age (Gompertz law) is followed by a period of deceleration, with slower rates of mortality increase (Gavrilov and Gavrilova 2011). This refutation of mortality deceleration was made using records from the U.S. Social Security Administration’s Death Master File (DMF).

Taking into account the significance of this finding for actuarial theory and practice, we tested these earlier observations using additional independent datasets and alternative statistical approaches. In particular, the following data sources for U.S. mortality at advanced ages were analyzed: (1) data from the Human Mortality Database (HMD) on age-specific death rates for 1890–99 U.S. birth cohorts, (2) recent extinct birth cohorts of U.S. men and women based on DMF data, and (3) mortality data for railroad retirees.

In the case of HMD data, the analyses were conducted for 1890–99 birth cohorts in the age range 80–106. Mortality was fitted by the Gompertz and logistic (Kannisto) models using weighted nonlinear regression and Akaike information criterion as the goodness-of-fit measure. All analyses were conducted separately for men and women. It was found that for all studied HMD birth cohorts, the Gompertz model demonstrated better fit of mortality data than the Kannisto model in the studied age interval. Similar results were obtained for U.S. men and women born in 1890–99 and railroad retirees born in 1895–99 using the full DMF file (obtained from the National Technical Information Service, or NTIS). It was also found that mortality estimates obtained from the DMF records are close to estimates obtained using the HMD cohort data.

An alternative approach for studying mortality patterns at advanced ages is based on calculating the age-specific rate of mortality change (life table aging rate, or LAR) after age 80. This approach was applied to age-specific death rates for Canada, France, Sweden and the United States available in HMD. It was found that for all 24 studied single-year birth cohorts, LAR does not change significantly with age in the age interval 80–100, suggesting no mortality deceleration in this interval. Simulation study of LAR demonstrated that the apparent decline of LAR after age 80 found in earlier studies may be related to biased estimates of mortality rates measured in a wide five-year age interval.

Taking into account that there exists several empirical estimates of hazard rate (Nelson-Aalen, actuarial and Sacher), a simulation study was conducted to find out which one is the most accurate and unbiased estimate of hazard rate at advanced ages. Computer simulations demonstrated that some estimates of mortality (Nelson-Aalen and actuarial) as well as kernel smoothing of hazard rates may produce spurious mortality deceleration at extreme ages, while the Sacher estimate turns out to be the most accurate estimate of hazard rate. Possible reasons for finding apparent mortality deceleration in earlier studies are also discussed.

This study was supported in part by National Institutes of Health grant R01 AG028620

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Saturday, February 14, 2015

Predictors of Exceptional Longevity

Dear Colleagues,

We are pleased to alert you that our new peer-reviewed study on Predictors of Exceptional Longevity is now publicly available at PubMed Central® (PMC) - a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318523/

Comments and suggestions are most welcome!

Kind regards,

-- Leonid and Natalia
-------------------------------------------------
-- Leonid Gavrilov, Ph.D., GSA Fellow
-- Natalia Gavrilova, Ph.D., GSA Fellow
Center on Aging, NORC at the University of Chicago
Website: http://longevity-science.org/

==========================================

Predictors of Exceptional Longevity:
Effects of Early-Life Childhood Conditions, Mid-Life Environment and Parental Characteristics
Gavrilov L.A., Gavrilova N.S.
In: 2014 Living to 100 Monograph [ available in PMC 2015 Feb 5 ].
The Society of Actuaries, 2014, 18 pages

Abstract:

Knowledge of strong predictors of mortality and longevity is very important for actuarial science and practice. Earlier studies found that parental characteristics as well as early-life conditions and midlife environment play a significant role in survival to advanced ages. However, little is known about the simultaneous effects of these three factors on longevity. This ongoing study attempts to fill this gap by comparing centenarians born in the United States in 1890–91 with peers born in the same years who died at age 65. The records for centenarians and controls were taken from computerized family histories, which were then linked to 1900 and 1930 U.S. censuses. As a result of this linkage procedure, 765 records of confirmed centenarians and 783 records of controls were obtained.

Analysis with multivariate logistic regression found that parental longevity and some midlife characteristics proved to be significant predictors of longevity while the role of childhood conditions was less important. More centenarians were born in the second half of the year compared to controls, suggesting early origins of longevity. We found the existence of both general and gender-specific predictors of human longevity. General predictors common for men and women are paternal and maternal longevity. Gender-specific predictors of male longevity are the farmer occupation at age 40, Northeastern region of birth in the United States and birth in the second half of year. A gender-specific predictor of female longevity is surprisingly the availability of radio in the household according to the 1930 U.S. census.

Given the importance of familial longevity as an independent predictor of survival to advanced ages, we conducted a comparative study of biological and nonbiological relatives of centenarians using a larger sample of 1,945 validated U.S. centenarians born in 1880–95. We found that male gender of centenarian has significant positive effect on survival of adult male relatives (brothers and fathers) but not female blood relatives. Life span of centenarian siblings-in-law is lower compared to life span of centenarian siblings and does not depend on centenarian gender. Wives of male centenarians (who share lifestyle and living conditions) have a significantly better survival compared to wives of centenarians’ brothers. This finding demonstrates an important role of shared familial environment and lifestyle in human longevity.

The results of this study suggest that familial background, early-life conditions and midlife characteristics play an important role in longevity. This study was supported by National Institutes of Health (NIH) grant R01 AG028620.

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