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Indian Journal of Community Medicine :... 2022Demographic indices known as the age-heaping indexes were used to explore the patterns of age misreporting in a multicentric survey.
INTRODUCTION
Demographic indices known as the age-heaping indexes were used to explore the patterns of age misreporting in a multicentric survey.
METHODS
The data of 3252 individuals were analyzed, and measurement of errors in age for the sampled data has been evaluated by Whipple's Index (WI), Myer's Blended Index, and United Nations Age-Sex Accuracy Score which comprises Sex Ratio Score, Male Age Ratio Score (ARS), and Female ARS.
RESULTS
Out of total 3252 participants, 828 (25.5%) were female. The mean statistical division age of our population was 34 (8.5) years and ranged from 15 to 65 years. The percentage of female ages ending with digits 0 or 5 is 23.55% and percentage of male ages ending with digits 0 or 5 is 23.28%. The calculated WI was 117.75 and 116.34 for males and females, respectively. The calculated Myer's Index for females and males is 10.53 and 25, respectively.
CONCLUSION
The study provides evidence that probably age-heaping bias is less of problem in the conducted study.
PubMed: 35368490
DOI: 10.4103/ijcm.ijcm_1179_21 -
Kidney International Reports Dec 2021Owing to organ shortage, the number of kidney transplantation (KT) involving older adult living donors is increasing. We aimed to investigate the effects of living-donor...
INTRODUCTION
Owing to organ shortage, the number of kidney transplantation (KT) involving older adult living donors is increasing. We aimed to investigate the effects of living-donor age and donor-recipient age differences on KT outcomes.
METHODS
This single-center, retrospective cohort study involved 853 adult LDKTs performed between January 2008 and December 2018. Recipients were stratified into the following 5 groups based on donor age and donor-recipient age difference: donor age, 30 to 49 years and age difference, -10 to 15 years; donor age, 50 to 69 years and age difference, -10 to 15 years; donor age, 50 to 69 years and age difference, 15 to 40 years; donor age, 70 to 89 years and age difference, -10 to 15 years; and donor age, 70 to 89 years and age difference, 15 to 40 years (groups 1, 2, 3, 4, and 5, respectively). As a primary outcome, the risk of graft loss was investigated. The secondary outcomes were postoperative estimated glomerular filtration rates (eGFRs) and mortality rates of recipients.
RESULTS
Group 4, representing KT between older adult donors and older adult recipients, had the highest graft loss risk and mortality. The eGFRs of the recipients from donors aged 70 to 89 years (groups 4 and 5) were significantly lower than those from donors in the other groups. Although the differences in the eGFR between groups 4 and 5 were not significant, the eGFR of group 4 was lower than that of group 5 at 6 months post-KT.
CONCLUSION
LDKTs from older adult donors to older adult recipients resulted in the worst graft survival and mortality rates.
PubMed: 34901571
DOI: 10.1016/j.ekir.2021.10.002 -
Bioinformation 2020Chronological age conveys only a rough approximation of the maturational status of a person whereas skeletal maturity indicators give a more accurate estimation....
Chronological age conveys only a rough approximation of the maturational status of a person whereas skeletal maturity indicators give a more accurate estimation. Therefore, it is of interest to document the correlation between chronological and skeletal age using CVMI and modified MP3 methods. A total of 39 subjects between the age ranges of 9-16 years were selected for this study. Pre-treatment lateral cephalograms and hand-wrist radiographs of the subjects were used. The skeletal age was analyzed by the Cervical Vertebrae Maturity Index (CVMI) and modified MP3 methods. The data was analyzed with SPSS software version 23.00. Kendall's Tau correlation test was performed to estimate the correlation between chronological age and skeletal age among the subjects and a linear regression test was also performed. Positive correlation was found between chronological age and skeletal age assessed by CVMI method (r= 0.398) and modified MP3 method (r=0.382) with p value >0.003. Thus it can be concluded that there was a positive correlation between chronological age and skeletal age among all the subjects.
PubMed: 34938004
DOI: 10.6026/973206300161045 -
Skin Research and Technology : Official... Aug 2023Age-related changes in scalp parameters affect hair quality and scalp condition. However, detailed data on biophysical parameters of the scalp across age groups remain...
BACKGROUND
Age-related changes in scalp parameters affect hair quality and scalp condition. However, detailed data on biophysical parameters of the scalp across age groups remain scarce. We aimed to investigate the differences in scalp parameters between individuals in their 20s and 50s and analyze their sex-specific variations.
MATERIALS AND METHODS
Two hundred participants (160 women and 40 men) were equally divided into 20s and 50s age groups. Biophysical parameters of the scalp, including elasticity, pH, trans-epidermal water loss (TEWL), sebum production, desquamation, firmness, redness, and yellowness, were measured in the vertex, occipital, and temporal regions. Hair density and thickness were measured in the temporal region. The accumulation of advanced glycation end products (AGEs) in the skin was noninvasively measured in a subset of 60 women.
RESULTS
Skin firmness and redness increased with age in women, whereas yellowness increased with age in both sexes. Sebum production and pH levels were significantly lower in the 50s age group than in the 20s age group, particularly in women. TEWL was lower in men in their 50s than in those in their 20s, particularly in the occipital region. A significant reduction in hair density was observed in the 50s age group in both sexes. AGE accumulation in the skin increased with age and was correlated with scalp skin yellowness.
CONCLUSION
Age-related changes in scalp parameters have important implications for hair health and scalp condition. These findings emphasize the importance of considering age and sex when developing hair care strategies.
Topics: Male; Female; Humans; Scalp; Skin; Hair; Epidermis; Biophysics
PubMed: 37632187
DOI: 10.1111/srt.13433 -
Frontiers in Psychology 2020Age attitudes and age stereotypes in the workplace can lead to discrimination and impaired productivity. Previous studies have predominantly assessed age stereotypes...
Age attitudes and age stereotypes in the workplace can lead to discrimination and impaired productivity. Previous studies have predominantly assessed age stereotypes with explicit measures. However, sole explicit measurement is insufficient because of social desirability and potential inaccessibility of stereotypical age evaluations to introspection. We aimed to advance the implicit and explicit assessment of work-related evaluations of age groups and age stereotypes and report data collected in three samples: students ( = 50), older adults ( = 53), and workers ( = 93). Evaluative age attitudes were measured implicitly with an Implicit Association Test. Regardless of group, age, and condition (neutral or semantically biased stimuli), the results confirm a stable, moderate implicitly measurable preference for younger over older workers. Whereas explicit measures of general age preferences showed no clear age preference, differentiated explicit measures of work-related age stereotypes also revealed stable preferences in all three samples: Younger workers were rated higher on performance and adaptability and older workers were rated higher on competence, reliability, and warmth. The explicit-implicit correlations were relatively low. Although explicit work-related age stereotypes are differentiated, the stable implicitly measured age bias raises concern. We suggest to apply implicit and explicit measures in the field of ageism in the workplace.
PubMed: 33123059
DOI: 10.3389/fpsyg.2020.579155 -
The International Journal of Angiology... Dec 2022Both systolic and diastolic blood pressures increase with age up to 50 to 60 years of age. After 60 years of age systolic pressure rises to 84 years of age but diastolic...
Both systolic and diastolic blood pressures increase with age up to 50 to 60 years of age. After 60 years of age systolic pressure rises to 84 years of age but diastolic pressure remains stable or even decreases. In the oldest age group (85-99 years), the systolic blood pressure (SBP) is high and diastolic pressure (DBP) is the lowest. Seventy percent of people older than 65 years are hypertensive. This paper deals with the role of advanced glycation end products (AGE) and its cell receptor (RAGE) and soluble receptor (sRAGE) in the development of hypertension in the elderly population. Plasma/serum levels of AGE are higher in older people as compared with younger people. Serum levels of AGE are positively correlated with age, arterial stiffness, and hypertension. Low serum levels of sRAGE are associated with arterial stiffness and hypertension. Levels of sRAGE are negatively correlated with age and blood pressure. Levels of sRAGE are lower in patients with arterial stiffness and hypertension than patients with high levels of sRAGE. AGE could induce hypertension through numerous mechanisms including, cross-linking with collagen, reduction of nitric oxide, increased expression of endothelin-1, and transforming growth factor-β (TGF-β). Interaction of AGE with RAGE could produce hypertension through the generation of reactive oxygen species, increased sympathetic activity, activation of nuclear factor-kB, and increased expression of cytokines, cell adhesion molecules, and TGF- β. In conclusion, the AGE-RAGE axis could be involved in hypertension in elderly people. Treatment for hypertension in elderly people should be targeted at reduction of AGE levels in the body, prevention of AGE formation, degradation of AGE in vivo, downregulation of RAGE expression, blockade of AGE-RAGE interaction, upregulation of sRAGE expression, and use of antioxidants.
PubMed: 36588874
DOI: 10.1055/s-0042-1756175 -
Frontiers in Public Health 2022Several studies have demonstrated that environmental factors, such as meteorological factors and air pollutants, are closely associated with epistaxis. However,...
OBJECTIVE
Several studies have demonstrated that environmental factors, such as meteorological factors and air pollutants, are closely associated with epistaxis. However, age-specific associations between environmental factors and epistaxis have not yet been evaluated. This study aimed to evaluate the associations between individual meteorological factors and air pollutants and epistaxis, by age.
STUDY DESIGN
A retrospective cohort study.
SETTING
Records of patients covered by the Korean National Health Insurance Service who visited our hospital for epistaxis between January 1, 2002, and December 31, 2015, were retrospectively reviewed.
METHODS
The 46,628 enrolled patients were divided into four age groups: age group 0 (<18 years, = 19,580); age group 1 (18-40 years, = 10,978); age group 2 (41-70 years, = 13,395); and age group 3 (>70 years, = 2,675). Cases of epistaxis and data on environmental factors were analyzed according to the day, month, and year. Stepwise logistic regression was performed to identify the environmental risk factors for epistaxis in each age group.
RESULTS
Age group 0 had the highest number of patients with epistaxis, whereas age group 3 had the lowest. Relative humidity, temperature, concentrations of particulate matter (PM10) and sulfur dioxide, sunshine duration, and wind speed were significantly associated with the occurrence of epistaxis in the study population. However, analysis according to age group showed that the meteorological factors and air pollutants associated with epistaxis were different in each age group.
CONCLUSION
We suggest that the environmental risk factors for epistaxis should be differentially analyzed according to age.
Topics: Humans; Adolescent; Infant; Child, Preschool; Child; Young Adult; Adult; Middle Aged; Aged; Retrospective Studies; Epistaxis; Air Pollutants; Particulate Matter; Age Factors
PubMed: 36339143
DOI: 10.3389/fpubh.2022.966461 -
Frontiers in Psychology 2019This study examines the world's Top 100 age class performance times by Master athletes in marathon running. The predominant paradigm for this type of research assumes...
This study examines the world's Top 100 age class performance times by Master athletes in marathon running. The predominant paradigm for this type of research assumes that the outcomes represent a "virtual" cross-sectional study with important implications about aging. This article critiques this perspective and presents alternative models that include temporal dimensions that relate to cohort differences, age changes and historical transitions. One purpose of this study is to compare these models with respect to goodness of fit to the data. A second purpose is to evaluate the generalizability of findings from the fastest divisional age class quartile to the slower quartiles. Archival listings by the Association of Road Racing Statisticians include a maximum of 100 fastest age class performances in marathon running performances by men and women. This database includes 937 performances by 387 men performances and 856 performances by 301 women. The mean ages are 62.05 years for men and 60.5 years for women. The mean numbers of performances per runner are 6.64 for men and 6.4 for women. Analysis by mixed linear modeling (MLM) indicates best goodness of fit for logarithms of performance time by a model that includes linear and quadratic expressions of age at entry into the database (termed "entry cohort") and subsequent age changes (termed "elapsed age") as variables. Findings with this model show higher performance times in women than men. Rates of increase in performance time are higher at older cohort ages and elapsed ages. Performance time increases with interactions between cohort age and elapsed age, cohort age and gender, and elapsed age and gender (i.e., with greater increases in women than men). Finally, increases in performance time with cohort age and elapsed age are higher in slower than faster performance quartiles, with athletes in the faster quartiles more likely to have multiple data entries and athletes in the slower quartiles single data entries. Implications of these findings are discussed.
PubMed: 31616350
DOI: 10.3389/fpsyg.2019.02161 -
Frontiers in Psychology 2022Few studies have addressed the longitudinal links between early temperament types and later problematic smartphone use. This study aims to identify children's early...
Few studies have addressed the longitudinal links between early temperament types and later problematic smartphone use. This study aims to identify children's early temperament types at age 3 and to examine the link between the temperament types and smartphone overdependence at age 10. This study utilized a population-based data set presented by the Panel Study on Korean Children. Based on emotionality, activity, and sociability levels at age 3, children were clustered into similar temperament types. Links between the early temperament types and the risks of smartphone overdependence at age 10 were identified through analyses of covariances and binary logistic regressions. Three early temperament types were identified among Korean children: reactive (28.1%), sociable (37.2%), and cautious (34.8%). Children's smartphone dependence at age 10 differed according to the temperament types identified at age 3. Compared to children with the sociable temperament type, children with the reactive type or the cautious type had an increased risk of smartphone overdependence. The link between temperament types at age 3 and smartphone overdependence at age 10 was meaningful. The cautious children were the most vulnerable group to the risk of smartphone overdependence. Temperament type identification in early years may be a useful measure for screening groups of children who are at risk for problematic smartphone use and need proactive interventions.
PubMed: 35360576
DOI: 10.3389/fpsyg.2022.833948 -
BMC Research Notes Feb 2022To establish reference data on required competition age regarding performance levels for both sexes, all swimming strokes, and race distances and to determine the effect...
OBJECTIVE
To establish reference data on required competition age regarding performance levels for both sexes, all swimming strokes, and race distances and to determine the effect of competition age on swimming performance in the context of other common age metrics. In total, 36,687,573 race times of 588,938 swimmers (age 14.2 ± 6.3 years) were analyzed. FINA (Fédération Internationale de Natation) points were calculated to compare race times between swimming strokes and race distances. The sum of all years of race participation determined competition age.
RESULTS
Across all events, swimmers reach top-elite level, i.e. > 900 FINA points, after approximately 8 years of competition participation. Multiple-linear regression analysis explained up to 40% of variance in the performance level and competition age showed a stable effect on all race distances for both sexes (β = 0.19 to 0.33). Increased race distance from 50 to 1500 m, decreased effects of chronological age (β = 0.48 to - 0.13) and increased relative age effects (β = 0.02 to 0.11). Reference data from the present study should be used to establish guidelines and set realistic goals for years of competition participation required to reach certain performance levels. Future studies need to analyze effects of transitions between various swimming strokes and race distances on peak performance.
Topics: Adolescent; Age Factors; Athletic Performance; Child; Female; Humans; Linear Models; Male; Swimming; Young Adult
PubMed: 35197115
DOI: 10.1186/s13104-022-05969-6