Age Validation in the Long Life Family Study Through a Linkage to Early-Life Census Records

Elo, Irma T.; Mykyta, Laryssa; Sebastiani, Paola; Christensen, Kaare; Glynn, Nancy W.; Perls, Thomas T.

doi:10.1093/geronb/gbt033

Cited by 23 publications

(23 citation statements)

References 20 publications

(22 reference statements)

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“…To ensure this, validation studies of age at cancer diagnosis or death must be carried out, especially among older populations. In the US, age misreporting among the oldest-old has been a concern, but several studies have found that age recorded on US death records is accurate also among the oldest individuals [15, 18]. A birth certificate is considered the gold standard for verifying an individual’s birth date, but for an old individual, the second best validation tool is often census data from the individual’s early life.…”

Section: Methodological Challenges In Studies Of Cancer In the Elderlmentioning

confidence: 99%

Cancer and aging: Epidemiology and methodological challenges

et al. 2016

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Epidemiological cancer data shed light on key questions within basic science, clinical medicine and public health. For decades, Denmark has had linkable health registers that contain individual level data on the entire population with virtually complete follow-up. This has enabled high quality studies of cancer epidemiology and minimized the challenges often faced in many countries, such as uncertain identification of the study base, age misreporting, and low validity of the cancer diagnoses. However, methodological challenges still remain to be addressed, especially in cancer epidemiology studies among the elderly and the oldest-old. E.g., a characteristic pattern for many cancer types is that the incidence increases up to a maximum at about ages 75 to 90 years and is then followed by a decline or a leveling off at the oldest ages. It has been suggested that the oldest individuals may be asymptomatic, or even insusceptible to cancer. An alternative interpretation is that this pattern is an artifact due to lower diagnostic intensity among the elderly and oldest-old caused by higher levels of co-morbidities in this age group. Currently, the available cancer epidemiology data are not able to provide clear evidence for any of these hypotheses.

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Section: Methodological Challenges In Studies Of Cancer In the Elderlmentioning

confidence: 99%

Cancer and aging: Epidemiology and methodological challenges

et al. 2016

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“…The aim of the LLFS is to identify genetic and phenotype traits that contribute to survival to extreme ages and subphenotypes of exceptional survival such as measures of healthy aging. Age reporting in the US LLFS sample has been shown to be of high quality [5]. Additional details about the LLFS such as phenotypic measures have been well described [6,7].…”

Section: Methodsmentioning

confidence: 99%

Extended maternal age at birth of last child and women’s longevity in the Long Life Family Study

et al. 2015

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Objective This study investigated the association between maternal ages at birth of last child and the likelihood of survival to advanced ages. Methods A nested case-control study using Long Life Family Study (LLFS) data. Three hundred and eleven women who survived past the oldest 5th percentile of survival according to birth cohort matched life tables were identified as cases and 151 women who died at ages younger than the top 5th percentile of survival were identified as controls. A Bayesian mixed-effect logistic regression model was used to estimate the association between maternal age at birth of last child and exceptional longevity among these 462 women. Results A significant association for later maternal age was found whereby women who had their last child beyond the age of 33 years had twice the odds of survival to the top 5th percentile of survival of their birth cohorts compared to women who had their last child by age 29 (OR=2.08, 95%CI 1.13; 3.92 for age between 33 and 37 years and OR=1.92, 95% CI 1.03; 3.68 for older age). Conclusion The study supports the findings from other studies demonstrating a positive association between older maternal age and greater odds of the mother surviving to unusually old age.

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“…In high-income countries (HICs), age misreporting affects primarily the oldest ages (Elo et al 2013) and/or the most disadvantaged groups. The reported age of centenarians has thus often been contested (Gessert, Elliott, and Haller 2002;Rosenwaike and Stone 2003).…”

Section: Age Misreporting In Demographic Studiesmentioning

confidence: 99%

“…In HICs, such issues with the quality of age data have been resolved through record linkages (Elo et al 2013). For some study participants, researchers seek to obtain independent, high-quality records of age for verification purposes.…”

Section: Age Misreporting In Demographic Studiesmentioning

confidence: 99%

“…The net effects of these age transfers are hard to predict, since they depend on (1) the direction and magnitude of age errors, and (2) the shape of the underlying 'true' age pyramid (Preston and Elo 1999). Most strategies to improve age data in demographic studies have focused on either verifying the age of participants through linkages with other records of an individual's date of birth (e.g., Elo et al 2013), or better training interviewers in eliciting age (Pullum 2006). The former strategy is, however, seldom feasible in LMICs, where independent records of age (e.g., early-life census records) are rarely available.…”

Section: Introductionmentioning

confidence: 99%

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Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal

Helleringer¹,

You²,

Fleury³

et al. 2019

DemRes

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BACKGROUND Age misreporting is pervasive in most low-and middle-income countries (LMIC). It may bias estimates of key demographic indicators, such as those required to track progress towards sustainable development goals. Existing methods to improve age data are often ineffective, cannot be adopted on a large scale, and/or do not permit estimating age over the entire life course. OBJECTIVE We tested a computer vision approach, which produces an age estimate by analyzing a photograph of an individual's face. METHODS We constituted a small training dataset in a population of Senegal covered by a health and demographic surveillance system (HDSS) since 1962. We collected facial images of 353 women aged 18 and above, whose age could be ascertained precisely using HDSS data. We developed automatic age estimation (AAE) systems through machine learning and cross-validation.

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Age Validation in the Long Life Family Study Through a Linkage to Early-Life Census Records

Cited by 23 publications

References 20 publications

Cancer and aging: Epidemiology and methodological challenges

Cancer and aging: Epidemiology and methodological challenges

Extended maternal age at birth of last child and women’s longevity in the Long Life Family Study

Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal

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