984 resultados para Dental age estimation
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This paper examines gender differences and trends over time in the age of initiation to heroin use. Data from two large surveys: the Sydney component of the ANAIDUS, conducted in 1989, and the ASHIDU, conducted in 1994, were used to examine this issue. Together, these studies contained information on 1,292 individuals who identified themselves as heroin users. Results indicated that, while there were no significant gender differences in age of initiation to heroin use, there was a significant (p < 0.001) time trend in the mean age at which heroin was first used. Specifically, the mean age of first heroin use among individuals born during the interval 1940-1949 was 20.5 years while among those born during 1970-1979 the mean age of first heroin use was 16.5 years. These findings were confirmed by analyses of the National Household Survey. Further analysis of the ASHIDU data indicated that younger age of initiation to heroin use was associated with polydrug use, overdose and crime after the effects of duration of heroin use had been statistically controlled. These findings suggest that there has been both an increase in the willingness of young people to experiment with heroin and an increased availability of the drug over this time. In combination with evidence that there has been an increase in the amount of heroin being imported into Australia, and an increased demand for treatment for opiate dependence, these data suggest that Australia is experiencing an increase in the use of heroin, particularly among youth.
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Background and objectives: The greatest increase in bone mineral content occurs during adolescence. The amount of bone accrued may significantly affect bone mineral status in later life. We carried out a longitudinal investigation of the magnitude and timing of peak bone mineral content velocity (PBMCV) in relation to peak height velocity (PHV) and the age at menarche in a group of adolescent girls over a 6-year period. Methods: The 53 girls in this study are a subset of the 115 girls (initially 8 to 16 years) in a g-year longitudinal study of bone mineral accretion. The ages at PBMCV and PHV were determined by using a cubic spline curve fitting procedure. Determinations were based on height (n = 12) and bone (n = 6) measurements over 6 years. Results: The timing of PBMCV and menarche were coincident, preceded approximately 1 year earlier by PHV. Correlation showed a negative relationship between age at menarche and both peak bone mineral accrual (r = -0.42, P
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In order to examine whether different populations show the same pattern of onset in the Southern Hemisphere, we examined the age-at-first-admission distribution for schizophrenia based on mental health registers from Australia and Brazil. Data on age-at-first-admission for individuals with schizophrenia were extracted from two names-linked registers, (1) the Queensland Mental Health Statistics System, Australia (N=7651, F= 3293, M=4358), and (2) a psychiatric hospital register in Pelotas, Brazil (N=4428, F=2220, M=2208). Age distributions were derived for males and females for both datasets. The general population structure tbr both countries was also obtained. There were significantly more males in the Queensland dataset (gz = 56.9, df3, p < 0.0001 ). Both dataset distributions were skewed to the right. Onset rose steeply after puberty to reach a modal age group of 20-29 for men and women, with a more gradual tail toward the older age groups. In Queensland 68% of women with schizophrenia had their first admissions after age 30, while the proportion from Brazil was 58%. Compared to the Australian dataset, the Brazilian dataset had a slightly greater proportion of first admissions under the age 30 and a slightly smaller proportion over the age of 60 years. This reflects the underlying age distributions of the two populations. This study confirms the wide age range and gender differences in age-at-first-admission distributions for schizophrenia and identified a significant difference in the gender ratio between the two datasets. Given widely differing health services, cultural practices, ethic variability, and the different underlying population distributions, the age-at-first-admission in Queensland and Brazil showed more similarities than differences. Acknowledgments: The Stanley Foundation supported this project.
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Age of onset is an important variable when considering the cause and course of mental illnesses. Given the debate about the relationship between psychotic disorders it would be useful to compare age-at-first-admission for ICD schizophrenia and for affective psychoses when the latter is differentiated into 'major depression' and 'bipolar disorder'. Data on age-at-first-admission for Australian-born individuals diagnosed with schizophrenia (ICD 295) or affective psychosis (ICD 296) were extracted from the Queensland Mental Health Statistics System -- a comprehensive, namelinked mental health register. Because the ICD 9 category 296.1 was used to code what is now called "major depressive episode', this group was differentiated from other 296 categorieswhich were considered bipolar disorders. Those receiving more than one diagnoses within these categories were excluded. All distributions show a wide age range of onset from early adolescence into the seventies and eighties. However the modal age-group for major depression ('60-69' for both sexes) is clearly different from bipolar disorder ('20-29' for males; '30- 39' for females), the latter distribution being more similar to the SCZ distribution (which had a model age-group of '20-29' for both sexes). While these distributions were similar for males and females, there were sex differences in the proportions within each diagnostic group: more males with schizophrenia, and more females with bipolar disorder and with major depression. Our results suggest heterogeneity within the affective psychoses as categorised by ICD 9, with bipolar disorder having an age-at-first-admission distribution more similar to schizophrenia than major depression. The Stanley Foundation supported this project.
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Background From the mid-1980s to mid-1990s, the WHO MONICA Project monitored coronary events and classic risk factors for coronary heart disease (CHD) in 38 populations from 21 countries. We assessed the extent to which changes in these risk factors explain the variation in the trends in coronary-event rates across the populations. Methods In men and women aged 35-64 years, non-fatal myocardial infarction and coronary deaths were registered continuously to assess trends in rates of coronary events. We carried out population surveys to estimate trends in risk factors. Trends in event rates were regressed on trends in risk score and in individual risk factors. Findings Smoking rates decreased in most male populations but trends were mixed in women; mean blood pressures and cholesterol concentrations decreased, body-mass index increased, and overall risk scores and coronary-event rates decreased. The model of trends in 10-year coronary-event rates against risk scores and single risk factors showed a poor fit, but this was improved with a 4-year time lag for coronary events. The explanatory power of the analyses was limited by imprecision of the estimates and homogeneity of trends in the study populations. Interpretation Changes in the classic risk factors seem to partly explain the variation in population trends in CHD. Residual variance is attributable to difficulties in measurement and analysis, including time lag, and to factors that were not included, such as medical interventions. The results support prevention policies based on the classic risk factors but suggest potential for prevention beyond these.
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Dendritic cells (DC) are considered to be the major cell type responsible for induction of primary immune responses. While they have been shown to play a critical role in eliciting allosensitization via the direct pathway, there is evidence that maturational and/or activational heterogeneity between DC in different donor organs may be crucial to allograft outcome. Despite such an important perceived role for DC, no accurate estimates of their number in commonly transplanted organs have been reported. Therefore, leukocytes and DC were visualized and enumerated in cryostat sections of normal mouse (C57BL/10, B10.BR, C3H) liver, heart, kidney and pancreas by immunohistochemistry (CD45 and MHC class II staining, respectively). Total immunopositive cell number and MHC class II+ cell density (C57BL/10 mice only) were estimated using established morphometric techniques - the fractionator and disector principles, respectively. Liver contained considerably more leukocytes (similar to 5-20 x 10(6)) and DC (similar to 1-3 x 10(6)) than the other organs examined (pancreas: similar to 0.6 x 10(6) and similar to 0.35 x 10(6): heart: similar to 0.8 x 10(6) and similar to 0.4 x 10(6); kidney similar to 1.2 x 10(6) and 0.65 x 10(6), respectively). In liver, DC comprised a lower proportion of all leukocytes (similar to 15-25%) than in the other parenchymal organs examined (similar to 40-60%). Comparatively, DC density in C57BL/10 mice was heart > kidney > pancreas much greater than liver (similar to 6.6 x 10(6), 5 x 10(6), 4.5 x 10(6) and 1.1 x 10(6) cells/cm(3), respectively). When compared to previously published data on allograft survival, the results indicate that the absolute number of MHC class II+ DC present in a donor organ is a poor predictor of graft outcome. Survival of solid organ allografts is more closely related to the density of the donor DC network within the graft. (C) 2000 Elsevier Science B.V. All rights reserved.
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Purpose: To examine age-related differences in the physical activity behaviors of young adults. Methods: We examined rates of participation in vigorous- and moderate-intensity leisure-time activity and walking, as well as an index of physical activity sufficient for health benefits in three Australian cross-sectional samples, for the age ranges of 18-19, 20-24, and 25-29 yr. Data were collected in 1991, 1996, and 1997/8. Results: There was at least a 15% difference in vigorous-intensity leisure-time physical activity from the 18-19 yr to the 25-29 yr age groups, and at least a 10% difference in moderate-intensity leisure-time physical activity. For the index of sufficient activity there was a difference between 9 and 21% across age groups. Differences in rates of walking were less than 8%. For all age groups, males had higher rates of participation for vigorous and moderate-intensity activity than did females, bur females had much higher rates of participation in walking than males. Age-associated differences in activity levels were more apparent for males. Conclusions: Promoting walking and various forms of moderate-intensity physical activities to young adult males, and encouraging young adult females to adopt other forms of moderate-intensity activity to complement walking may help to ameliorate decreases in physical activity over the adult lifespan.
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Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.
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We present a method of estimating HIV incidence rates in epidemic situations from data on age-specific prevalence and changes in the overall prevalence over time. The method is applied to women attending antenatal clinics in Hlabisa, a rural district of KwaZulu/Natal, South Africa, where transmission of HIV is overwhelmingly through heterosexual contact. A model which gives age-specific prevalence rates in the presence of a progressing epidemic is fitted to prevalence data for 1998 using maximum likelihood methods and used to derive the age-specific incidence. Error estimates are obtained using a Monte Carlo procedure. Although the method is quite general some simplifying assumptions are made concerning the form of the risk function and sensitivity analyses are performed to explore the importance of these assumptions. The analysis shows that in 1998 the annual incidence of infection per susceptible woman increased from 5.4 per cent (3.3-8.5 per cent; here and elsewhere ranges give 95 per cent confidence limits) at age 15 years to 24.5 per cent (20.6-29.1 per cent) at age 22 years and declined to 1.3 per cent (0.5-2.9 per cent) at age 50 years; standardized to a uniform age distribution, the overall incidence per susceptible woman aged 15 to 59 was 11.4 per cent (10.0-13.1 per cent); per women in the population it was 8.4 per cent (7.3-9.5 per cent). Standardized to the age distribution of the female population the average incidence per woman was 9.6 per cent (8.4-11.0 per cent); standardized to the age distribution of women attending antenatal clinics, it was 11.3 per cent (9.8-13.3 per cent). The estimated incidence depends on the values used for the epidemic growth rate and the AIDS related mortality. To ensure that, for this population, errors in these two parameters change the age specific estimates of the annual incidence by less than the standard deviation of the estimates of the age specific incidence, the AIDS related mortality should be known to within +/-50 per cent and the epidemic growth rate to within +/-25 per cent, both of which conditions are met. In the absence of cohort studies to measure the incidence of HIV infection directly, useful estimates of the age-specific incidence can be obtained from cross-sectional, age-specific prevalence data and repeat cross-sectional data on the overall prevalence of HIV infection. Several assumptions were made because of the lack of data but sensitivity analyses show that they are unlikely to affect the overall estimates significantly. These estimates are important in assessing the magnitude of the public health problem, for designing vaccine trials and for evaluating the impact of interventions. Copyright (C) 2001 John Wiley & Sons, Ltd.
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Gelation of UHT milk during storage (age gelation) is a major factor limiting its shelf-life. The gel which forms is a three-dimensional protein matrix initiated by interactions between the whey protein beta -lactoglobulin and the kappa -casein of the casein micelle during the high heat treatment. These interactions lead to the formation of a beta -lactoglobulin-kappa -casein complex (beta kappa -complex). A feasible mechanism of age gelation is based on a two-step process; in the first step, the beta kappa -complexes dissociate from the casein micelles due to the breakdown of multiple anchor sites on kappa -casein, and in the second step, these complexes aggregate into a three-dimensional matrix. When a critical volume concentration of the beta kappa -complex is attained, a gel of custard-like consistency is formed. Significant factors which influence the onset of gelation include the nature of the heat treatment, proteolysis during storage, milk composition and quality, seasonal milk production factors and storage temperature. In this review, age gelation is discussed in terms of these factors, causative mechanisms and procedures for controlling it.