age gap
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2022 ◽  
Author(s):  
Zhuoting Zhu ◽  
Yifan Chen ◽  
Wei Wang ◽  
Yueye Wang ◽  
Wenyi Hu ◽  
...  

Background: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for ageing and mortality. Objective: To investigate associations of retinal age gap with arterial stiffness index (ASI) and incident cardiovascular disease (CVD). Methods: A deep learning (DL) model was trained based on 19,200 fundus images of 11,052 participants without any past medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35,917 participants. Regression models were used to assess the association between retinal age gap and ASI. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. Results: We found each one-year increase in retinal age gap was associated with increased ASI (β=0.002, 95% confidence interval [CI]: 0.001-0.003, P<0.001). After a median follow-up of 5.83 years (interquartile range [IQR]: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each one-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio [HR]=1.03, 95% CI: 1.01-1.06, P=0.012). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (HR=1.05; 95% CI, 1.00-1.10; P-overall <0.0001; P-nonlinear=0.0681). Conclusion: We found that retinal age gap was significantly associated with ASI and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.


2021 ◽  
Author(s):  
Seung-yeoun Kang ◽  
Jeong-hoon Mo

BACKGROUND Similarity-based machine-learning methodologies are suitable for personalized prediction and recommendation research, which is actively applied in healthcare field along with the generalization of EHR data. In particular, the similarity learning model which carefully reflects age can be efficiently used in predicting chronic diseases, closely related to ageing. OBJECTIVE We aimed to design a similarity model for patients in different age-groups in order to predict the two major chronic diseases: Diabetes and Hypertension. METHODS We developed an idea about learning the overlapping periods of two individuals by moving the viewpoint of them to future and past respectively. From this idea, we build separated similarity learning models through three sequential age-group intervals; 30-40, 40-50, 50-60 age-groups intervals. Each model has same structure based on deep neural network. For similarity learning, we set several demographic/bi-annual check-up information and diagnosis records as input features and disease based yes-or-no similarity labels as output features. RESULTS As a result of applying hypertension patients’ pair, diabetes patients’ pair, and non-diabetes/diabetes patient pair to our methodology, the similarity value was very high, close to 1 in the former two cases, and the similarity value was low, close to zero, in the last case. This proves that similarity learning appropriately reflects the disease status between individuals. In addition, we tried to find out how the conventional single-timepoint methodology and our methodology differ in the measurement of similarity for several special cases in which the patient's disease condition changes. As a result, it was found that the similarity results between the existing methodology and our methodology differ from at least 0.2 to at most 0.9 in four special cases where the patient's condition changes. This suggests that our methodology responds more sensitively to the patient's condition changing over time and can be applied more efficiently to disease prediction in those cases. CONCLUSIONS We developed an age-sensitive similarity learning model for personalized prediction of chronic diseases targeting Koreans. As a result, for the cases that patient's disease pattern changes, by designing and learning a deep similarity learning model using divided age groups which has not been previously attempted, we have shown that similarity learning results are better than conventional single-timepoint methodology. Moreover, we proposed the possibility of overcoming data shortage limitations that occur frequently in medical datasets through a similarity learning model considering patients’ age differences.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 40-40
Author(s):  
Eugene Dim ◽  
Markus Schafer

Abstract Gerontologists have long documented how age is associated with political participation. However, few studies have considered how macrocontextual factors shape participation across the life span. Moreover, very few studies have dealt with political engagement and aging in emerging democracies, including those in Africa. This study addresses those gaps, integrating the most recent three waves of Afrobarometer survey data (2011–2018) with country-level data from the freedom house (i.e. freedom index). Findings reveal that, at the individual level, an age gap widens for engagement in protests and shrinks for electoral and non-electoral political participation. When the political context is considered, however, we find that political freedom softens the drop-off of protest behavior at later ages. For electoral and non-electoral political participation, we find that freer countries lessen the expected growth in engagement across the life span. The study implies that political oppression shapes the links between age and political behaviour, but the processes seem different depending on whether they are engaging in risky (where the age gap widens) or non-risky (where the age gap shrinks) political forms of engagement.


2021 ◽  
pp. 110190
Author(s):  
Ying Feng ◽  
Jie Ren
Keyword(s):  

2021 ◽  
Author(s):  
Naomi Havron ◽  
Irena Lovcevic ◽  
Michelle Z.L Kee ◽  
Helen Chen ◽  
Yap Seng Chong ◽  
...  

Previous literature has shown that family structure affects language development. Here, factors relating to older siblings (their presence in the house, sex and age gap), mothers (maternal stress) and household size and residential crowding were examined to systematically examine the different role of these factors. Data from mother-child dyads in a Singaporean birth cohort, (677-855 dyads; 52% males; 58-61% Chinese, 20-24% Malay, 17-19% Indian) collected when children were 24-, 48-, and 54-months old, were analysed. There was a negative effect of having an older sibling, moderated by the siblings’ age gap, but not by the older sibling’s sex, nor household size or residential crowding. Maternal stress affected language outcomes in some analyses but not others. Implications for understanding the effect of family structure on language development are discussed.


2021 ◽  
Author(s):  
Weiqi Man ◽  
Hao Ding ◽  
Chao Chai ◽  
Xingwei An ◽  
Feng Liu ◽  
...  

2021 ◽  
Author(s):  
Naomi Havron ◽  
Irena Lovcevic ◽  
Michelle Z.L Kee ◽  
Helen Chen ◽  
Yap Seng Chong ◽  
...  

Previous literature has shown that family structure affects language development. Here, factors relating to older siblings (their presence in the house, sex and age gap), mothers (maternal stress) and household size and residential crowding were examined to systematically examine the different role of these factors. Data from mother-child dyads in a Singaporean birth cohort, (677-855 dyads; 52% males; 58-61% Chinese, 20-24% Malay, 17-19% Indian) collected when children were 24-, 48-, and 54-months old, were analysed. There was a negative effect of having an older sibling, moderated by the siblings’ age gap, but not by the older sibling’s sex, nor household size or residential crowding. Maternal stress affected language outcomes in some analyses but not others. Implications for understanding the effect of family structure on language development are discussed.


2021 ◽  
Vol 13 (4) ◽  
pp. 121-151
Author(s):  
Nai Peng Tey

This paper uses matched couple data from the 1991, 2000, and 2010 population censuses to examine the changes in spousal differentials in age, education, and work status, as well as inter-ethnic and international marriages. The general trend is one of decreasing spousal age and educational gaps between 1991 and 2010. Although older-man younger-woman marriages still predominated, the spousal age gap decreased from 4.6 years to 3.9 years, and the proportion of marriages in which the husband was more than 6 years older than the wife declined from 30% to 24%. Educational homogamy (couples having the same educational level) rose from 53% to 64%, while the proportion of women marrying someone of higher education declined from 33% to 21%. Inter-ethnic marriage hovered around 4.2% throughout the study period, after rising from less than 1% in the 1980s. International marriages made up about 1.2% of all marriages in 2010, up from 0.8% in 1991. The labour force participation rate of married women had increased significantly, resulting in the rise of dual-income households. The changing spousal differentials in socio-demographic characteristics are bound to alter gender roles and relations that will impact Malaysia’s family institution and demographic outcomes.


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