Childhood Conditions and Exceptional Longevity
See also:
What is Aging? Theories of Aging Population Aging
Greetings,
I am pleased to share with you our new research findings:
"Childhood Conditions and Exceptional Longevity"
http://paa2006.princeton.edu/download.aspx?submissionId=61675
and
http://longevity-science.org/Centenarians-PAA-2006.pdf
Any comments and suggestions are welcome!
In brief, we have found that the early-life childhood conditions are indeed very important in determining the chances of exceptional longevity in later life, and we will present our findings at the Population Association of America meeting in Los Angeles, on Saturday, April 1:
http://paa2006.princeton.edu/sessionViewer.aspx?sessionId=1251
Here is the Power-Point Presentation:
"Childhood Conditions and Exceptional Longevity"
http://longevity-science.org/PAA-2006.ppt
Hope to see you there!
Kind regards,
-- Leonid Gavrilov, Ph.D.
Website: http://longevity-science.org/
Blog: http://longevity-science.blogspot.com/
-------------
P.S.: Here is a brief description of our study:
A number of previous studies found interesting links between the early-life experiences and a subsequent mortality in later life. Such findings justify further advancement of these studies by exploring possible links between childhood conditions and exceptional longevity (survival to 100 years). However a number of methodological issues have to be resolved (such as data availability, quality, as well as research approaches), before starting a comprehensive research project on childhood predictors of exceptional longevity. This paper represents an attempt to do a preliminary study of related methodological issues in order to ensure feasibility of the subsequent large-scale research efforts. The following questions are explored in this study: Where to get data on exceptional longevity and childhood conditions? What is the quality of this data, and how can this data quality be checked and improved? What methods of data analysis to use? What are the hypotheses to test, and what are the preliminary findings to validate?
The study presents a detailed analysis of available data resources on exceptional longevity and childhood conditions, a rigorous evaluation of data quality and testing different approaches to improve the quality of the data. As a result of these methodological explorations the following multi-step procedure of data collection and data cleaning has being suggested and tested in practice:
Step 1. To extract data on alleged centenarians and their childhood conditions from computerized genealogies, pre-selected on the basis of their expected sufficient data quality (a number of indicators of data quality has been identified for this purpose). Following this procedure we extracted detailed family data for 991 alleged centenarians born in 1875-1899 in the United States from publicly available computerized genealogies of 75 million individuals identified in our previous study (Gavrilova, Gavrilov, 1999).
Step 2. To validate the claims of exceptional longevity by cross-checking these records with the US Social Security Administration database on deceased person for the entire US population.
Step 3. To double-check the validated centenarian claims again for the accuracy of birth date information by matching the records with the early US Censuses (1900, 1910, 1920 and 1930).
Step 4. To enhance further the quality of records with confirmed exceptional longevity by adding information on childhood conditions available in the early US Censuses.
This multi-step procedure of data collection and quality evaluation is described in this paper in great detail, to demonstrate how this approach works in practice. A new validated dataset on exceptional longevity and childhood conditions has been successfully developed in this study using this multi-step procedure of data collection and validation. This validated dataset contains information on 485 centenarians born in the United States in 1890-1900 and the families where they were raised. Thus, this multi-step approach has been tested in practice and it could be recommended for a subsequent large-scale research project on childhood predictors of exceptional longevity.
At the next step of this study we used the collected and validated dataset on exceptional longevity and childhood conditions as a sandbox for applying different methods of data analysis and testing a number of specific hypotheses about childhood predictors of exceptional longevity. Specifically, we followed the earlier studies by Preston and Haines (1991) who found that the lowest sickness burden in early life (measured through the level of child mortality) was observed in the families of farmers, and families living in the Western region of the United States in the 1900s. This leads to a testable prediction that centenarians should occur more often in the families of farmers, and in families living in the Western region of the United States, if more healthy childhood conditions are indeed conductive for exceptional longevity later in life. We tested these hypotheses using a method of multiple logistic regression to compare household characteristics of 'centenarian' families in the 1900s with household characteristics of representative sample of the 1900 US Census (IPUMPS data). Indeed it turned out that centenarians were born more often in the families of farmers, and in families living in the Western region as predicted by the 'childhood conditions' hypothesis.
We also explored different approaches to study the effects of the birth order on exceptional longevity, starting with analysis of a simple summary statistic like the 'centenarian birth order ratio' and the 'centenarian birth order difference', followed by more sophisticated analysis based on multiple logistic regression. These studies revealed that there is a statistically significant association between the chances of exceptional survival and the birth order.
Finally, a method of within-family analysis has been applied to investigate the occurrence patterns for centenarians among siblings, which allows researchers to avoid confounding caused by between-family variation. This approach was implemented using conditional logistic regression with a binary outcome variable describing either a centenarian, or non-centenarian survival outcome. For this in-depth analysis the 198 validated centenarians born in USA in 1890-1893 were identified, and their family histories were reconstructed using the US Censuses, the US Social Security Administration database, genealogical records and other supplementary data resources. The following predictor variables were explored: sex, birth order, paternal age at person's birth, maternal age at person's birth, and the season of birth.
The study found that first-born siblings are more likely to become centenarians when compared to later-born siblings (odds ratio = 1.77, 95% CI = 1.18 - 2.66, P = 0.006). This protective effect of first-born status can not be simply explained by differences in child mortality, because it persists when a comparison is made with those siblings only who survived to adulthood (age 20): odds ratio = 1.95, 95% CI = 1.26 - 3.01, P = 0.003. Moreover, even at age 75 it still helps to be a first-born child in order to become a centenarian (odds ratio = 1.66, 95% CI = 1.02 - 2.69, P = 0.04).
In order to find out the mechanism of the birth-order effect, a multivariate analysis with included parental age variables was performed. This multivariate analysis found that the protective effect of being first-born is driven mostly by the young maternal age at person's birth (being born to mother younger than 25 years). Being born to young mother is the major predictor of human longevity (odds ratio = 2.03, 95% CI = 1.33 - 3.11, P = 0.001). Moreover, even at age 75 it is still important to be born to young mother in order to survive to 100 years (odds ratio = 1.87, 95% CI = 1.15 -3.05, P = 0.01).
The results of this study demonstrate that childhood conditions are indeed very important in determining the chances of exceptional longevity and justify the feasibility of the subsequent large-scale research efforts in this direction.
What is Aging? Theories of Aging Population Aging
Greetings,
I am pleased to share with you our new research findings:
"Childhood Conditions and Exceptional Longevity"
http://paa2006.princeton.edu/download.aspx?submissionId=61675
and
http://longevity-science.org/Centenarians-PAA-2006.pdf
Any comments and suggestions are welcome!
In brief, we have found that the early-life childhood conditions are indeed very important in determining the chances of exceptional longevity in later life, and we will present our findings at the Population Association of America meeting in Los Angeles, on Saturday, April 1:
http://paa2006.princeton.edu/sessionViewer.aspx?sessionId=1251
Here is the Power-Point Presentation:
"Childhood Conditions and Exceptional Longevity"
http://longevity-science.org/PAA-2006.ppt
Hope to see you there!
Kind regards,
-- Leonid Gavrilov, Ph.D.
Website: http://longevity-science.org/
Blog: http://longevity-science.blogspot.com/
-------------
P.S.: Here is a brief description of our study:
A number of previous studies found interesting links between the early-life experiences and a subsequent mortality in later life. Such findings justify further advancement of these studies by exploring possible links between childhood conditions and exceptional longevity (survival to 100 years). However a number of methodological issues have to be resolved (such as data availability, quality, as well as research approaches), before starting a comprehensive research project on childhood predictors of exceptional longevity. This paper represents an attempt to do a preliminary study of related methodological issues in order to ensure feasibility of the subsequent large-scale research efforts. The following questions are explored in this study: Where to get data on exceptional longevity and childhood conditions? What is the quality of this data, and how can this data quality be checked and improved? What methods of data analysis to use? What are the hypotheses to test, and what are the preliminary findings to validate?
The study presents a detailed analysis of available data resources on exceptional longevity and childhood conditions, a rigorous evaluation of data quality and testing different approaches to improve the quality of the data. As a result of these methodological explorations the following multi-step procedure of data collection and data cleaning has being suggested and tested in practice:
Step 1. To extract data on alleged centenarians and their childhood conditions from computerized genealogies, pre-selected on the basis of their expected sufficient data quality (a number of indicators of data quality has been identified for this purpose). Following this procedure we extracted detailed family data for 991 alleged centenarians born in 1875-1899 in the United States from publicly available computerized genealogies of 75 million individuals identified in our previous study (Gavrilova, Gavrilov, 1999).
Step 2. To validate the claims of exceptional longevity by cross-checking these records with the US Social Security Administration database on deceased person for the entire US population.
Step 3. To double-check the validated centenarian claims again for the accuracy of birth date information by matching the records with the early US Censuses (1900, 1910, 1920 and 1930).
Step 4. To enhance further the quality of records with confirmed exceptional longevity by adding information on childhood conditions available in the early US Censuses.
This multi-step procedure of data collection and quality evaluation is described in this paper in great detail, to demonstrate how this approach works in practice. A new validated dataset on exceptional longevity and childhood conditions has been successfully developed in this study using this multi-step procedure of data collection and validation. This validated dataset contains information on 485 centenarians born in the United States in 1890-1900 and the families where they were raised. Thus, this multi-step approach has been tested in practice and it could be recommended for a subsequent large-scale research project on childhood predictors of exceptional longevity.
At the next step of this study we used the collected and validated dataset on exceptional longevity and childhood conditions as a sandbox for applying different methods of data analysis and testing a number of specific hypotheses about childhood predictors of exceptional longevity. Specifically, we followed the earlier studies by Preston and Haines (1991) who found that the lowest sickness burden in early life (measured through the level of child mortality) was observed in the families of farmers, and families living in the Western region of the United States in the 1900s. This leads to a testable prediction that centenarians should occur more often in the families of farmers, and in families living in the Western region of the United States, if more healthy childhood conditions are indeed conductive for exceptional longevity later in life. We tested these hypotheses using a method of multiple logistic regression to compare household characteristics of 'centenarian' families in the 1900s with household characteristics of representative sample of the 1900 US Census (IPUMPS data). Indeed it turned out that centenarians were born more often in the families of farmers, and in families living in the Western region as predicted by the 'childhood conditions' hypothesis.
We also explored different approaches to study the effects of the birth order on exceptional longevity, starting with analysis of a simple summary statistic like the 'centenarian birth order ratio' and the 'centenarian birth order difference', followed by more sophisticated analysis based on multiple logistic regression. These studies revealed that there is a statistically significant association between the chances of exceptional survival and the birth order.
Finally, a method of within-family analysis has been applied to investigate the occurrence patterns for centenarians among siblings, which allows researchers to avoid confounding caused by between-family variation. This approach was implemented using conditional logistic regression with a binary outcome variable describing either a centenarian, or non-centenarian survival outcome. For this in-depth analysis the 198 validated centenarians born in USA in 1890-1893 were identified, and their family histories were reconstructed using the US Censuses, the US Social Security Administration database, genealogical records and other supplementary data resources. The following predictor variables were explored: sex, birth order, paternal age at person's birth, maternal age at person's birth, and the season of birth.
The study found that first-born siblings are more likely to become centenarians when compared to later-born siblings (odds ratio = 1.77, 95% CI = 1.18 - 2.66, P = 0.006). This protective effect of first-born status can not be simply explained by differences in child mortality, because it persists when a comparison is made with those siblings only who survived to adulthood (age 20): odds ratio = 1.95, 95% CI = 1.26 - 3.01, P = 0.003. Moreover, even at age 75 it still helps to be a first-born child in order to become a centenarian (odds ratio = 1.66, 95% CI = 1.02 - 2.69, P = 0.04).
In order to find out the mechanism of the birth-order effect, a multivariate analysis with included parental age variables was performed. This multivariate analysis found that the protective effect of being first-born is driven mostly by the young maternal age at person's birth (being born to mother younger than 25 years). Being born to young mother is the major predictor of human longevity (odds ratio = 2.03, 95% CI = 1.33 - 3.11, P = 0.001). Moreover, even at age 75 it is still important to be born to young mother in order to survive to 100 years (odds ratio = 1.87, 95% CI = 1.15 -3.05, P = 0.01).
The results of this study demonstrate that childhood conditions are indeed very important in determining the chances of exceptional longevity and justify the feasibility of the subsequent large-scale research efforts in this direction.
posted by Dr. Leonid Gavrilov, Ph.D. at 12:08 PM
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