• Anatomy and Cell Biology
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• v.51 no.4
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• pp.251-259
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• 2018

The aim of this study is to examine the correlation between tooth wear and age by quantitatively measuring maxillary first molar wear in children. A total of 150 maxillary dental models were analyzed in 30 subjects (male, 11; female, 19) with an age range of 6-14 years. Maxillary first molar wear were assessed based on area, volume and the shortest distance from the buccal occlusal plane to the central pit point (BCPH). The area and volume of the tooth cusps were measured at four different offset-plane heights (0.2, 0.4, 0.6, and 0.8 mm). Relationship between age and the amount of wear or BCPH were statistically analyzed. Correlation and regression analyses were also performed, and age estimation was obtained with linear regression analysis. Repeated measures analysis of variance (ANOVA) revealed significant differences between age and the amount of wear based on area, volume, and offset-plane height. Except age of 8 and 10, 12 and 14's 0.2-mm offset-plane-measured volume, all area and volume measurement of all ages and offset-plane height showed a significant amount of increase. Wear speeds were calculated using the BCPH. Among age and measurement variables, the correlation coefficient was strongest when the volume was measured from the 0.4-mm offset-plane. As age increases, the amount of wear, as quantified by area and volume measurements, also increases. According to this study, a regression equation that can be used for age estimation is follows: Age $(y)=0.16{\times}0.4V+0.85$ ($R^2=0.490$) using volume.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.349-356
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• 2015

The study is to recognize the interactions with bone ages by measuring the length between the end of the bone and the growth plate on selected highest weight of regions of seven for bone maturity in TW3 method. The experiment is subjected on seventy-two children (36 males, 36 females) who have examined the growth plate test from March, 2014 to March, 2015 and implemented a regression analysis by measuring the length between the end of the bone and the growth plate in Hand AP image of the children. In result, each bone age has produced a mean value and a standard deviation corresponding to the specific range and as bone age increases the length between the end of the bone and the growth plate decreased. In addition, female children showed lower mean value in comparison to male and also the measurement of the length between the end of the bone and the growth plate and its bone age are shown to be statistically valid(p<0.001) according to the results of regression analysis using its result value. Therefore, the probability of prediction on the bone age read off through the applied TW3 method and regression equation in the Hand AP image of the children.

• Yonsei Medical Journal
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• v.59 no.9
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• pp.1115-1122
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• 2018

Purpose: To investigate biomechanical properties of the cornea using a dynamic Scheimpflug analyzer according to age. Materials and Methods: In this prospective, cross-sectional, observational study, participants underwent ophthalmic investigations including corneal biomechanical properties, keratometric values, intraocular pressure (IOP), and manifest refraction spherical equivalent (MRSE). We determined the relationship of biomechanical parameters and ocular/systemic variables (participant's age, MRSE, IOP, and mean keratometric values) by piecewise regression analysis, association of biomechanical parameters with variables by Spearman's correlation and stepwise multiple regression analyses, and reference intervals (RI) by the bootstrap method. Results: This study included 217 eyes of 118 participants (20-81 years of age). Piecewise regression analysis between Corvis-central corneal thickness (CCT) and participant's age revealed that the optimal cut-off value of age was 45 years. No clear breakpoints were detected between the corneal biomechanical parameters and MRSE, IOP, and mean keratometric values. Corneal velocity, deformation amplitude, radius, maximal concave power, Corvis-CCT, and Corvis-IOP exhibited correlations with IOP, regardless of age (all ages, 20-44 years, and over 44 years). With smaller deformation amplitude and corneal velocity as well as increased CorvisIOP and Corvis-CCT, IOP became significantly increased. We provided the results of determination of confidence interval from RI data using bootstrap method in three separate age groups (all ages, 20-44 years, and over 44 years). Conclusion: We demonstrated multiple corneal biomechanical parameters according to age, and reported that the corneal biomechanical parameters are influenced by IOP.

• Journal of Preventive Medicine and Public Health
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• v.6 no.1
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• pp.77-79
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• 1973

The Mechanism for sorting out the covariance effect is known as the covariance analysis. The sorting out of regression and correlation effect is an obvious application of the covariance analysis. The result of Do population by age groups (15 ages interval) from 1966 Census and from 1970 Census has been applied to analyzing covariability by the analysis of covariance. The results are as follows. (1) The signicance of the regression of 1970 population on 1966 population is assured as F=116.5 (2) There is a significant difference between mean of each age group. (F=88.1) (3) There is very little evidence of significant heterogeneity of regression between age groups. (F=0.72)

• Korean Journal of Poultry Science
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• v.20 no.4
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• pp.203-208
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• 1993

The objective of this study was to investigate the correlation among the measurements of the body weight and shank length at the age of 0, 4, 8, 12, 16 and 20 wk, and to investigate the regression of the final body weight at the age of 20 wk in selection on the body weight and shank length before 12 wk of age. From the simple correlation analysis, the range of correlation coefficients between body weight and shank length at the same age were 0.50~0.83 from females, and 0.57~0.85 from males over all wk of age(P＜0.01). Correlation coefficient between the body weights at hatch and 20 wk of age was 0.44(P＜0.01), but it was not significant(P＞0.05) between the shank length at hatch and body weight at 20 wk of age. The favorable regression models for the estimation of the body weight at the age of 20 wk from both body weight and shank length before 12 wk of age were the models with the independent variables of measurements at hatch and 12 wk of age($R^2$＝0.96), with the measurements at 8 and 12 wk of age($R^2$＝0.96), and with the measurements at 0, 8 and 12 wk of age ($R^2$＝0.96).

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7569-7573
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• 2014

The main objective of this study was to analyze the mortality trends of female breast cancer in Turkey between the years 1987-2008. The rates per 100,000 age-standardized to the European standard population were assessed and time trends presented using joinpoint regression analysis. Average annual percent change (AAPC), anual percent change (APC) and 95% confidence interval (CI) was calculated. Nearly 23,000 breast cancer deaths occurred in Turkey during the period 1987-2008, with the average annual age-standardized mortality rate (ASR) being 11.9 per 100,000 women. In the last five years, significant increases were observed in all age groups, but there was no significant change over the age of 65. In this period, the biggest significant increase was in the 45-54 age group (AAPC=4.3, 95%CI=2.6 to 6.0).

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.276-282
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• 2015

Fetal cerebellum is grow depending on the gestational age, measurement of transverse cerebellar diameter(TCD) is being used import indicator of fetal growth prediction in clinical. In this study, the subjects were normal pregnant women 20~37 week of gestation, and the volume scan was conducted on the 340 subjects. The research reports was indicated by regression curve the growth of fetal TCD in accordance with the gestational age. It got to the value of the results from a linear regression equation. Measurement fetal TCD using 3D US was statistically significant(p<0.001) and useful in the prediction of gestational age. TCD increases with gestational age can also distinguish between the normal fetal and prediction of accurate gestational age of fetal growth retardation. If the basic data of the present study, ongoing research is performed, the TCD using by 3D US are expected to be usefully applied in the correct prediction gestational age.

• Korean Journal of Poultry Science
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• v.22 no.1
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• pp.1-6
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• 1995

A total of 514 birds were used to investigate the influence of body weight and shank length at hatch on the body weights at various ages in growing pheasant. Statistical model included the terms of hatch and sex as fixed effects and the two covariates of body weight and shank length at hatch. In this model, the effects of hatch and sex on the body weights at the age of 4, 8, 12, 16 and 20 wk, and the average daily gains from hatch to 8 wk and from 8 to 16 wk of age were highly significant(P<0.01). All the regression coefficients of body weights and average daily gains on the body weight at hatch were also significant(P<0.01). Their estimates were 3.05.7.21. 13.89, 15.18 and 15.33 for the body weights at 4. 8, 12, 16 and 20 wk of age ; 0.111 and 0.142 for the average daily gains from hatch to 8 wk, and from 8 to 16 wk of age, respectively. On the shank length, only the regression coefficients of the body weights at 4 and 8 wk of age and the average daily gains from hatch to 8 wk of age were significant(P<0.01). Results of this study suggest that body weight at hatch do significantly affect the body weights in the growing periods up to' the 20 wk of age, but the shank length at hatch influences the body weights only at early age.1)

• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.49-59
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• 1997

Backgrounds : There has been many studies investigating the causes of farmers' syndrome. In some studies, psychologic stress is related to farmer's syndrome. And the diagnostic criteria of farmer's syndrome is similar to those of generalized anxiety disorder. So we carried out this study to investigate the psychiatric causes of farmers' syndrome. Methods : This study was done in some rural and urban areas of Kyoungsangnam Province, July, August, October and November of 1996. Those who came to free medical service and completed interview, medical examination and laboratory examination and 20-59 years old were 150 persons. And those who came to health center for health examination and completed only interview and 20-59 years old were 94 persons. The questionnaire was composed of sociodemographic factors, health risk factors, farmer's syndrome, work load, BEPSI(inventory to measure stress), Spielberger's state-trait anxiety inventory, self-rating depression scale. To examine statistical significance, we used X2-test, Mantel-Haenszel test for linear association, t-test, ANCOVA, correlation, multiple regression, logistic regression. Results : The prevalence of farmer's syndrome, adjusted for age and sex with population of Kyungsangnam Province of 1993 was 208 per 1,000(90 per 1,000 in men and 329 per 1,000 in women). In bivariate analysis, farmer's syndrome was significantly related to age, sex, job, income, smoking, alcohol drinking, work load, BEPSI, state anxiety, trait anxiety, depression, body mass index, Hwa-Byung, hypertension, anemia. However, when age and sex were adjusted, job was not significantly related to farmer's syndrome. The score of farmer's syndrome was significantly related to age, sex, work load, BEPSI, trait anxiety by multiple regression. Farmer's syndrome was significantly related to increasing age(odd ratio 1.079, 95% C.I. 1.060 - 1.099), sex(odds ratio of male 0.434, 95% C.I. 0.349 - 0.540), and BEPSI(odds ratio 1.231, 95% C.I. 1.148 - 1.320) by logistic regression. Results of logistic regression analysis of the component symptoms of farmer's syndrome were as follows. Shoulder stiffness was significantly related to increasing age, female sex and BEPSI. Lumbago was significantly related to increasing age, female sex and trait anxiety. Numb limbs and nocturia was significantly related to increasing age and female sex. Breathlessness was significantly related to work load, sleeplessness was significantly related to depression, dizziness was significantly related to job and state anxiety, and abdominal fullness was significantly related to female sex. Conclusion : Farmers' syndrome was related to work load, but was more related to psychiatric factors such as BEPSI and trait anxiety. And the occupation was not risk factor of farmers' syndrome in this study, so further study is needed to investigate the cause of farmers' syndrome.

• Health Policy and Management
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• v.4 no.2
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• pp.58-76
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• 1994

In order to study factors affecting the use of the ambulatory physician services. Andersen's model for health utilization was modified by adding the health behavior component and examined with three different approaches. Three different approaches were the multiople regression model, logistic regression model, and LISREL model. For multiple regression, dependent variable was reported illness-related visits to a physician during past one year and independent variables are variaous variables measuring predisposing factor, enabling factor, need factor and health behavior. For the logistic regression, dependent variable was visit or no-visit to a physician during past one year and independent variables were same as the multiple regression analysis. For the LISREL, five endogenous variables of health utiliztion, predisposing factor, enabling factor, need factor, and health behavior and 20 exogeneous variables which measures five endogenous variables were used. According to the multiple regression analysis, chronic illness, health status, perceived health status of the need factor; residence, sex, age, marital status, education of the predisposing factor ; health insurance, usual source for medical care of enabling factor were the siginificant exploratory variables for the health utilization. Out of the logistic regression analysis, health status, chronic illness, residence, marital status, education, drinking, use of health aid were found to be significant exploratory variables. From LISREL, need factor affect utilization most following by predisposing factor, enabling factor and health behavior. For LISREL model, age, education, and residence for predisposing factor; health status, chronic illess, and perceived health status for need factor; medical insurance for enabling factor; and doing any kind of health behavior for the health behavior were found as the significant observed variables for each theoretical variables.