8 resultados para distribution patterns
em DigitalCommons@The Texas Medical Center
Resumo:
Prostate cancer is the most common incident cancer and the second leading cause of death in men in the United States. Although numerous attempts have been made to identify risk factors associated with prostate cancer, the results have been inconsistent and conflicting. The only established risk factors are age and ethnicity. A positive family history of prostate cancer has also been shown to increase the risk two- to three-fold among close relatives.^ There are several similarities between breast and prostate cancer that make the relationship between the two of interest. (1) Histologically, both cancers are predominantly adenocarcinomas, (2) both organs have a sexual and/or reproductive role, (3) both cancers occur in hormone-responsive tissue, (4) therapy often consists of hormonal manipulation, (5) worldwide distribution patterns of prostate and breast cancer are positively correlated.^ A family history study was conducted to evaluate the aggregation of prostate cancer and co-aggregation of breast cancer in 149 patients referred to The University of Texas, M.D. Anderson Cancer Center with newly diagnosed prostate cancer. All patients were white, less than 75 years of age at diagnosis and permanent residents of the United States. Through a personal interview with the proband, family histories were collected on 1,128 first-degree relatives. Cancer diagnoses were verified through medical records or death certificate. Standardized incidence ratios were calculated using a computer program by Monson incorporating data from Connecticut Tumor Registry.^ In this study, familial aggregation of prostate cancer was verified only among the brothers, not among fathers. Although a statistically significant excess of breast cancer was not found, the increased point estimates in mothers, sisters and daughters are consistent with a co-aggregation hypothesis. Rather surprising was the finding of a seven-fold increased risk of prostate cancer and a three-fold increased risk of breast cancer among siblings in the presence of a maternal history of any cancer. Larger family history studies including high risk (African-Americans) and lower-risk groups (Hispanics) and incorporating molecular genetic evaluations should be conducted to determine if genetic differences play a role in the differential incidence rates across ethnic groups. ^
Resumo:
Objective: To determine the prevalence of and the relationships between the degree and source of hyperandrogenemia, ovulatory patterns and cardiovascular disease risk indicators (blood pressure, indices or amount of obesity and fat distribution) in women with menstrual irregularities seen at endocrinologists' clinic. Design: A cross-sectional study design. Participants: A sample of 159 women with menstrual irregularities, aged 15-44, seen at endocrinologists' clinic. Main Outcome Measures: androgen levels, body mass index (BMI), waist-hip ratio (WHR), systolic and diastolic blood pressure (SBP & DBP), source of androgens, ovulatory activity. Results: The prevalence of hyperandrogenemia was 54.7% in this study sample. As expected, women with acne or hirsutism had an odds ratio 12.5 (95%CI = 5.2-25.5) times and 36 (95%CI = 12.9-99.5) times more likely to have hyperandrogenemia than those without acne or hirsutism. The main findings of this study were the following: Hyperandrogenemic women were more likely to have oligomenorrheic cycles (OR = 3.8, 95%CI = 1.5-9.9), anovulatory cycles (OR = 6.6, 95%CI = 2.8-15.4), general obesity (BMI $\ge$ 27) (OR = 6.8, 95%CI = 2.2-27.2) and central obesity (WHR $\ge$ 127) (OR = 14.5, 95%CI = 6.1-38.7) than euandrogenemic women. Hyperandrogenemic women with non-suppressible androgens had a higher mean BMI (29.3 $\pm$ 8.9) than those with suppressible androgens (27.9 $\pm$ 7.9); the converse was true for abdominal adiposity (WHR). Hyperandrogenemic women had a 2.4 odds ratio (95%CI = 1.0-6.2) for an elevated SBP and a 2.7 odds ratio (95%CI = 0.8-8.8) for elevated DBP. When age differences were accounted for, this relationship was strengthened and further strengthened when sources of androgens were controlled. When the differences in BMI were controlled, the odds ratio for elevated SBP in hyperandrogenemic women increased to 8.8 (95%CI = 1.1-69.9). When the age, the source of androgens, the amount of obesity and the type of obesity were controlled, hyperandrogenemic women had 13.5 (95%CI = 1.1-158.9) odds ratio for elevated SBP. Conclusions: In this study population, the presence of menstrual irregularities are highly predictive for the presence of elevated androgens. Women with elevated androgens have a high risk for obesity, more specifically for central obesity. The androgenemic status is an independent predictor of blood pressure elevation. It is probable that in the general population, the presence of menstrual irregularities are predictive of hyperandrogenemia. There is a great need for a population study of the prevalence of hyperandrogenemia and for longitudinal studies in hyperandrogenemic women (adrenarche to menopause) to investigate the evolution of these relationships. ^
Resumo:
This study retrospectively evaluated the spatial and temporal disease patterns associated with influenza-like illness (ILI), positive rapid influenza antigen detection tests (RIDT), and confirmed H1N1 S-OIV cases reported to the Cameron County Department of Health and Human Services between April 26 and May 13, 2009 using the space-time permutation scan statistic software SaTScan in conjunction with geographical information system (GIS) software ArcGIS 9.3. The rate and age-adjusted relative risk of each influenza measure was calculated and a cluster analysis was conducted to determine the geographic regions with statistically higher incidence of disease. A Poisson distribution model was developed to identify the effect that socioeconomic status, population density, and certain population attributes of a census block-group had on that area's frequency of S-OIV confirmed cases over the entire outbreak. Predominant among the spatiotemporal analyses of ILI, RIDT and S-OIV cases in Cameron County is the consistent pattern of a high concentration of cases along the southern border with Mexico. These findings in conjunction with the slight northward space-time shifts of ILI and RIDT cluster centers highlight the southern border as the primary site for public health interventions. Finally, the community-based multiple regression model revealed that three factors—percentage of the population under age 15, average household size, and the number of high school graduates over age 25—were significantly associated with laboratory-confirmed S-OIV in the Lower Rio Grande Valley. Together, these findings underscore the need for community-based surveillance, improve our understanding of the distribution of the burden of influenza within the community, and have implications for vaccination and community outreach initiatives.^
Resumo:
Longitudinal principal components analyses on a combination of four subcutaneous skinfolds (biceps, triceps, subscapular and suprailiac) were performed using data from the London Longitudinal Growth Study. The main objectives were to discover at what age during growth sex differences in body fat distribution occur and to see if there is continuity in body fatness and body fat distribution from childhood into the adult status (18 years). The analyses were done for four age sectors (3mon-3yrs, 3yrs-8yrs, 8yrs-18yrs and 3yrs-18yrs). Longitudinal principal component one (LPC1) for each age interval in both sexes represents the population mean fat curve. Component two (LPC2) is a velocity of fatness component. Component three (LPC3) in the 3mon-3yrs age sector represents infant fat wave in both sexes. In the next two age sectors component three in males represents peaks and shifts in fat growth (change in velocity), while in females it represents body fat distribution. Component four (LPC4) in the same two age sectors is a reversal in the sexes of the patterns seen for component three, i.e., in males it is body fat distribution and in females velocity shifts. Components five and above represent more complicated patterns of change (multiple increases and decreases across the age interval). In both sexes there is strong tracking in fatness from middle childhood to adolescence. In males only there is also a low to moderate tracking of infant fat with middle to late childhood fat. These data are strongly supported in the literature. Several factors are known to predict adult fatness among the most important being previous levels of fatness (at earlier ages) and the age at rebound. In addition we found that the velocity of fat change in middle childhood was highly predictive of later fatness (r $\approx -$0.7), even more so than age at rebound (r $\approx -$0.5). In contrast to fatness (LPC1), body fat distribution (LPC3-LPC4) did not track well even though significant components of body fat distribution occur at each age. Tracking of body fat distribution was higher in females than males. Sex differences in body fat distribution are non existent. Some sex differences are evident with the peripheral-to-central ratios after age 14 years. ^
Resumo:
Sizes and power of selected two-sample tests of the equality of survival distributions are compared by simulation for small samples from unequally, randomly-censored exponential distributions. The tests investigated include parametric tests (F, Score, Likelihood, Asymptotic), logrank tests (Mantel, Peto-Peto), and Wilcoxon-Type tests (Gehan, Prentice). Equal sized samples, n = 18, 16, 32 with 1000 (size) and 500 (power) simulation trials, are compared for 16 combinations of the censoring proportions 0%, 20%, 40%, and 60%. For n = 8 and 16, the Asymptotic, Peto-Peto, and Wilcoxon tests perform at nominal 5% size expectations, but the F, Score and Mantel tests exceeded 5% size confidence limits for 1/3 of the censoring combinations. For n = 32, all tests showed proper size, with the Peto-Peto test most conservative in the presence of unequal censoring. Powers of all tests are compared for exponential hazard ratios of 1.4 and 2.0. There is little difference in power characteristics of the tests within the classes of tests considered. The Mantel test showed 90% to 95% power efficiency relative to parametric tests. Wilcoxon-type tests have the lowest relative power but are robust to differential censoring patterns. A modified Peto-Peto test shows power comparable to the Mantel test. For n = 32, a specific Weibull-exponential comparison of crossing survival curves suggests that the relative powers of logrank and Wilcoxon-type tests are dependent on the scale parameter of the Weibull distribution. Wilcoxon-type tests appear more powerful than logrank tests in the case of late-crossing and less powerful for early-crossing survival curves. Guidelines for the appropriate selection of two-sample tests are given. ^
Resumo:
This study evaluates the effect of a specially designed, physician-oriented handbook of antimicrobial use on the prescribing patterns of a group of fifty doctors at a university hospital. Data were evaluated over a peroid of one-and-one-half years, before and after the distribution of the handbook. For the purposes of this study, antimicrobial therapy was classified: (1) inappropriate if it violated one of a number of recognized principles of antimicrobial therapy, (2) appropriate if it agreed with specific recommendations or alternatives given in the distributed reference handbook, and (3) acceptable if it was neither inappropriate nor appropriate as defined by the handbook. An initial survey of antimicrobial prescribing patterns was made. Five months later the handbook was distributed and a two-week orientation program, consisting of the distribution and promotion of the problem-oriented, pocket-size handbook of appropriate antimicrobial therapy, was conducted. The handbook, which was developed by the authors and reviewed and approved by a panel of infectious disease specialists, presented guidelines for appropriate and efficacious usage of antimicrobial agents as most currently accepted in common clinical infections. Subsequent surveys were then conducted two weeks, three months, and six months after distribution of the handbook. A statistically significant difference (p < 0.01) in antimicrobial prescribing patterns was noted between the survey conducted two weeks after the introduction of the handbook and the other surveys. In this survey, while therapy classified inappropriate decreased from 44% to 28%, therapy appropriate as recommended increased from 31% to 53%. The findings of this study demonstrate that the introduction and promotion of the handbook decreases abuse and increases proper use of antimicrobial therapy, although the effect is sustainable for only a short duration--no longer than three months. These results indicate the need for a vigorous, updated program to achieve and maintain current appropriate antibotic therapy in clinical medicine. ^
Resumo:
The pattern of body fat distribution known as "centralized", and characterized by a predominance of subcutaneous fat on the trunk and a "pot belly", has been associated with an increased risk of chronic disease. These patterns of fat distribution, as well as the lifestyle habit variables associated with adult fatness and chronic morbidity clearly begin to develop during childhood, indicating the need for intervention and primary prevention of obesity, particularly the centralized form, during childhood or adolescence. The purpose of this study was to determine whether regular aerobic exercise could beneficially alter the distribution of body fat in 8 and 9 year old children. One hundred and eighty-eight participants were randomized into either a regular aerobic exercise treatment group or a standard physical education program control group. A variety of aerobic activities was used for intervention 5 days per week during physical education class for a period of 12 weeks. Fat distribution was measured by a number of the most commonly used indices, including ratios of body circumferences and skinfolds and indices derived from a principal components analysis. Change over time in average pulse rate was used to determine if intervention actually occurred. Approximately 10% of the students were remeasured, allowing the calculation of intra- and interexaminer measurement reliability estimates for all indices.^ This study group was comparable to the U.S. population, though the study children were slightly larger for certain measures. No effect of the exercise intervention was found. The most likely explanation for this was inadequacy of the intervention, as indicated by the lack of any change in average pulse rate with treatment. The results of the measurement reliability analysis are reported and indicate that body circumference ratios are more precise than skinfold ratios, particularly when multiple observers are used. Reliability estimates for the principal component indices were also high.^ It remains unclear whether the distribution of body fat can be altered with exercise. It is likely that this issue will remain undecided until one highly reliable, valid, and sensitive measure of fat distribution can be found. ^
Resumo:
The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^