scholarly journals A life course approach to total tooth loss: Testing the sensitive period, accumulation, and social mobility models in the Health and Retirement Study

2019 ◽  
Vol 47 (4) ◽  
pp. 333-339 ◽  
Author(s):  
Haena Lee
2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S382-S382
Author(s):  
Yu-Chih Chen ◽  
Sojung Park ◽  
Nancy Morrow-Howell

Abstract Wealth, an important financial cushion for older adults to buffer economic stress, requires a longer time to accumulate and develop in one’s course of life. However, little is known about the trajectories of wealth in later life, and how the life course socioeconomic status (SES) may contribute to the development of wealth at old-age. This study investigated longitudinal patterns of wealth trajectory and whether SES across the life course affects these trajectories using critical period, accumulation, and social mobility models. Using data from 16,189 adults aged 51 and older from the 2004-2014 Health and Retirement Study, a growth mixture model was used to explore distinct wealth trajectories. Impacts of life course models were studied using multinomial logistic regression. Results showed that four heterogeneous latent classes of wealth were identified: Stable high (reference group), Low and increasing, Stable low, and High but decline. Disadvantaged adulthood SES, accumulated exposure to socioeconomic risks, and downward or persistent socioeconomic disadvantage over the life course were associated with Stable low, Low and increasing, and High but decline, supporting all three life course mechanisms on wealth development in later life. Evidence suggests that wealth development is heterogeneous across individuals, and a strong gradient effect of life-course SES on wealth trajectories are clearly observed. Programs and policies should address the effects of life course on wealth development to strengthen the economic well-being in later life.


2019 ◽  
Vol 32 (7-8) ◽  
pp. 753-763
Author(s):  
Aniruddha Das

Objectives: Rather than acting as a buffer, educational attainment has a known positive linkage with major experiences of lifetime discrimination. Recently established genetic roots of education, then, may also influence such reports. The current study examined these patterns. Methods: Data were from the 2010 wave of the Health and Retirement Study. Polygenic scores indexed one’s genetic propensity for more education. Mediation analysis was through counterfactual methods. Results: Among Whites as well as Blacks, genetic antecedents of education also elevated discrimination reports. Part of this influence was channeled through education. At least among Whites, direct effects were also found. Discussion: Major discrimination experiences seem partly rooted in genes. Mechanisms are tentatively suggested. Direct genetic influences, in particular, indicate potential confounding of previously estimated linkages between discrimination and health or life course factors. Given the range of these prior results, and their implications for healthy aging, investigation of these possibilities is needed.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Raphaële Castagné ◽  
Cyrille Delpierre ◽  
Michelle Kelly-Irving ◽  
Gianluca Campanella ◽  
Florence Guida ◽  
...  

PLoS Medicine ◽  
2019 ◽  
Vol 16 (6) ◽  
pp. e1002827 ◽  
Author(s):  
Zuyun Liu ◽  
Xi Chen ◽  
Thomas M. Gill ◽  
Chao Ma ◽  
Eileen M. Crimmins ◽  
...  

2020 ◽  
Vol 11 ◽  
pp. 100587
Author(s):  
Kate A. Duchowny ◽  
Margaret T. Hicken ◽  
Peggy M. Cawthon ◽  
M. Maria Glymour ◽  
Philippa Clarke

2020 ◽  
Vol 99 (3) ◽  
pp. 257-263 ◽  
Author(s):  
R.K. Celeste ◽  
H.S. Eyjólfsdóttir ◽  
C. Lennartsson ◽  
J. Fritzell

We compared socioeconomic life course models to decompose the direct and mediated effects of socioeconomic status (SES) in different periods of life on late-life oral health. We used data from 2 longitudinal Swedish studies: the Level of Living Survey and the Swedish Panel Study of Living Conditions of the Oldest Old. Two birth cohorts (older, 1925 to 1934; younger, 1944 to 1953) were followed between 1968 and 2011 with 6 waves. SES was measured with 4 indicators of SES and modeled as a latent variable. Self-reported oral health was based on a tooth conditions question. Variables in the younger and older cohorts were grouped into 4 periods: childhood, young/mid-adulthood, mid /late adulthood, late adulthood/life. We used structural equation modeling to fit the following into lagged-effects life course models: 1) chain of risk, 2) sensitive period with late-life effect, 3) sensitive period with early- and late-life effects, 4) accumulation of risks with cross-sectional effects, and 5) accumulation of risks. Chain of risk was incorporated into all models and combined with accumulation, with cross-sectional effects yielding the best fit (older cohort: comparative fit index = 0.98, Tucker-Lewis index = 0.98, root mean square error of approximation = 0.04, weighted root mean square residual = 1.51). For the older cohort, the chain of SES from childhood → mid-adulthood → late adulthood → late life showed the following respective standardized coefficients: 053, 0.92, and 0.97. The total effect of childhood SES on late-life tooth loss (standardized coefficient: –0.23 for older cohort, –0.17 for younger cohort) was mediated by previous tooth loss and SES. Cross-sectional effects of SES on tooth loss were observed throughout the life course, but the strongest coefficients were at young/mid-adulthood (standardized coefficient: –0.41 for older cohort, –0.45 for younger cohort). SES affects oral health cumulatively over the life course and through a chain of risks. Actions to improve socioeconomic conditions in early life might have long-lasting effects on health if they help prevent people from becoming trapped in a chain of risks.


2019 ◽  
Vol 48 (Supplement_3) ◽  
pp. iii17-iii65
Author(s):  
Sinead McLoughlin ◽  
Cathal McCrory

Abstract Background Allostatic load (AL) is a measure of cumulative physiological dysregulation that is posited to capture the ‘wear and tear’ on the body resulting from exposure to chronic stress. AL has been shown to predict disease, morbidity and mortality. Multiple studies have shown an inverse relationship between AL and SEP, but few have examined the life course social patterning of AL. Methods Using baseline data from The Irish Longitudinal Study on Ageing (TILDA), an AL index was calculated by summing the number of biomarkers for which respondents fell within high risk quartiles. 17 biomarkers were examined, representing cardiovascular, immune, metabolic and parasympathetic nervous systems. SEP and life course trajectories were determined using father’s occupation (at age 14) and current occupation, which were aggregated to create four categories of social mobility; stable high, downwardly mobile, upwardly mobile and stable low. Negative binomial regression models were fitted for each of the life course models of critical period, accumulation and social mobility, to examine the associations between SEP and AL (n=3,282). Results Higher SEP was associated with lower AL. A significant association between origin SEP and later life remained after controlling for destination SEP. The ‘stable high’ across the life course had the lowest AL burden, the ‘stable low’ had the highest burden, and the mobile groups ranked intermediate. Conclusion Findings suggest childhood to be a sensitive period for the embedding of early life disadvantage. The accumulation hypothesis suggests that those who spend more time disadvantaged fair worse in terms of health. This study supports this hypothesis, as those who were stable low / stable high were in the worst / best health respectively.


2021 ◽  
Vol 37 (10) ◽  
Author(s):  
Luna Strieder Vieira ◽  
Juliana dos Santos Vaz ◽  
Fernando César Wehrmeister ◽  
Felipe Garcia Ribeiro ◽  
Janaína Vieira dos Santos Motta ◽  
...  

Abstract: This article aims to assess the relationship between an individual’s socioeconomic status over their life-course and their body mass index (BMI) at 22 years of age, according to the hypotheses generated by risk accumulation, critical period, and social mobility models. This was a population-based prospective study based on the Pelotas (Brazil) 1993 birth cohort. The risk accumulation, critical period, and social mobility models were tested in relation to a saturated model and compared with a partial F-test. After the best model was chosen, linear regression was carried out to determine the crude and adjusted regression coefficients of the association between socioeconomic status over the life-course and BMI at 22 years of age. The sample was comprised of 3,292 individuals (53.3% women). We found dose-response effect for both men and women, although the results were opposite. Among men, a lower score in socioeconomic status accumulation model led to a lower BMI average at 22 years of age; whereas among women, a lower score in socioeconomic status accumulation model caused an increase in BMI at 22 years of age.


2020 ◽  
Vol 44 (1) ◽  
pp. 100-117 ◽  
Author(s):  
Hidehiro Sugisawa ◽  
Ken Harada ◽  
Yoko Sugihara ◽  
Shizuko Yanagisawa ◽  
Masaya Shimmei

Objectives: In this study, we examined Japanese older adults' health habits (healthy diet, exercise, and nonsmoking) using 4 models: sensitive period, pathway, social mobility, and cumulative effects. Methods: A representative cross-sectional survey of people 65 years and older, living in Tokyo, produced 739 effective respondents. Health habits in social networks over the life course, at junior high school, age 20, and age 40, were measured through retrospective recall questions. Ordinary regression and logistic regression were used separately to analyze healthy diet and exercise/nonsmoking. Results: Regarding pathway effects, standardized coefficients of indirect health habits in social networks on late-life health habits were healthy diet = .073 (p < .05) and exercise = .125 (p < .001). Regarding social mobility effects, standardized coefficients of change to poorer health habits in social networks over the life course on late-life health habits, compared to maintaining healthy habits were healthy diet = -.121 (p < .01) and exercise e= -.235 (p < .05). Regarding cumulative effects, standardized coefficients of no exposure to better health habits in social networks over the life course were healthy diet = -0.103 (p < .01) and exercise = -.395 (p < .01). Conclusions: Three models – pathway, social mobility, and cumulative effects – may explain how healthy diet and exercise in social networks over the life course influence these health habits in later life.


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