6 resultados para Race time

em DigitalCommons@The Texas Medical Center


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The determinants of change in blood pressure during childhood and adolescence were studied in a cohort of U.S. national probability sample of 2146 children examined on two occasions during the Health Examination Survey. Significant negative correlations between the initial level and the subsequent changes in blood pressure were observed. The multiple regression analyses showed that the major determinants of systolic blood pressure (SBP) change were change in weight, baseline SBP, and baseline upper arm girth. Race, time interval between examinations, baseline age, and height change were also significant determinants in SBP change. For the change in diastolic blood pressure (DBP), baseline DBP, baseline weight, and weight change were the major determinants. Baseline SBP, time interval and race were also significant determinants. Sexual maturation variables were also considered in the subgroup analysis for girls. Weight change was the most important predictor of the change in SBP for the group of girls who were still in the pre-menarchal or pre-breast maturation status at the time of the follow-up examination, and who had started to menstruate or to develop breast maturation at sometime between the two examinations. Baseline triceps skinfold thickness or initial SBP were more important variables than weight change for the group of girls who had already experienced menarche or breast maturation at the time of the initial survey. For the total group, pubic hair maturation was found to be a significant predictor of SBP change at the 5% significance level. The importance of weight change and baseline weight for the changes in blood pressure warrants further study. ^

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This descriptive systematic review describes intervention trials for children and youth that targeted screen time (ST) as a way to prevent or control obesity and measured ST, and at least one of the following: physical activity, dietary intake, and adiposity. Both “hands-on” (e.g., video games) and “hands free” (e.g., television viewing) ST were included. Published, completed intervention trials (k=12), not-yet-published, completed trials (k=6), and in-progress trials (k=11) were identified through searches of electronic databases, including trial registries and bibliographies of eligible study reports. Study characteristics of the 29 identified trials were coded and presented in evidence tables. Considerable attention was paid to the type of ST addressed, measures used, and the type of interventions. Based on the number of in-progress and not-yet-published trials, the number of completed, published reports will double in the next three years. Most of the studies were funded by federal sources. General populations, not restricted by race, gender, or weight status, were targets of most interventions with children ages 9-12 yeas as the modal age group. Most trials used randomized control trials in which the majority of control or comparison group received an intervention. The mean number of participants was 242.8 (SD=314.7) and interventions were delivered over an average of 10.5 months and consisted of approximately 16 sessions, with a total time of about eight hours. The majority of completed trials evaluate each of the four constructs, however, most studies have more than one measure to assess each construct (e.g., BMI and tricep skinfold thickness to evaluate adiposity) and rarely did studies use the same measures. This is likely why the majority of studies produced at least one significant intervention effect on each outcome that was assessed. The four major outcomes should be evaluated in all interventions attempting to reduce screen time in order to determine the mechanisms involved that may contribute to obesity. More importantly researchers should work together to determine the best measures to evaluate the four main constructs to allow studies to be compared. Another area for consensus is the definition of ST. ^

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Background. The purpose of this study was to describe the risk factors and demographics of persons with salmonellosis and shigellosis and to investigate both seasonal and spatial variations in the occurrence of these infections in Texas from 2000 to 2004, utilizing time series analyses and the geographic information system digital mapping methods. ^ Methods. Spatial Analysis: MapInfo software was used to map the distribution of age-adjusted rates of reported shigellosis and salmonellosis in Texas from 2000–2004 by zip codes. Census data on above or below poverty level, household income, highest level of educational attainment, race, ethnicity, and urban/rural community status was obtained from the 2000 Decennial Census for each zip code. The zip codes with the upper 10% and lower 10% were compared using t-tests and logistic regression to determine whether there were any potential risk factors. ^ Temporal analysis. Seasonal patterns in the prevalence of infections in Texas from 2000 to 2003 were determined by performing time-series analysis on the numbers of cases of salmonellosis and shigellosis. A linear regression was also performed to assess for trends in the incidence of each disease, along with auto-correlation and multi-component cosinor analysis. ^ Results. Spatial analysis: Analysis by general linear model showed a significant association between infection rates and age, with young children aged less than 5 and those aged 5–9 years having increased risk of infection for both disease conditions. The data demonstrated that those populations with high percentages of people who attained a higher than high school education were less likely to be represented in zip codes with high rates of shigellosis. However, for salmonellosis, logistic regression models indicated that when compared to populations with high percentages of non-high school graduates, having a high school diploma or equivalent increased the odds of having a high rate of infection. ^ Temporal analysis. For shigellosis, multi-component cosinor analyses were used to determine the approximated cosine curve which represented a statistically significant representation of the time series data for all age groups by sex. The shigellosis results show 2 peaks, with a major peak occurring in June and a secondary peak appearing around October. Salmonellosis results showed a single peak and trough in all age groups with the peak occurring in August and the trough occurring in February. ^ Conclusion. The results from this study can be used by public health agencies to determine the timing of public health awareness programs and interventions in order to prevent salmonellosis and shigellosis from occurring. Because young children depend on adults for their meals, it is important to increase the awareness of day-care workers and new parents about modes of transmission and hygienic methods of food preparation and storage. ^

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The rates of syphilis in the United States have increased since the all time low in 2000. In 2003, the rates of syphilis in the United States were 2.5 cases per 100,000. There were 178 reported cases of primary and secondary syphilis (8.9 cases per 100,000) in Houston, Texas, which was a 58.9% increase from 2002. While syphilis can be completely treated now, unlike in times past, it is still a public health concern. The purpose of this study is to examine the possibility of modeling the impact of an immune response in primary and secondary syphilis in 63 major cities across the United States, stratified by gender and racial-ethnic groups. A Fourier analysis will be performed by SAS. Subsequently, this study will compare the results to a similar study of syphilis in 68 US cities, that focused on immune response, however, did not stratified by race and gender. This study will help determine if the oscillating rates of syphilis are due to biological factors of the disease or to behavioral changes in the population. This study will use surveillance data from 63 major cities across the United States. The data will be provided by the Centers of Disease Control. Ultimately, this study will expand the knowledge of the effect of immunity on endemics.^

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Recent data have shown that the percentage of time spent preparing food has decreased during the past few years, and little information is know about how much time people spend grocery shopping. Food that is pre-prepared is often higher in calories and fat compared to foods prepared at home from scratch. It has been suggested that, because of the higher energy and total fat levels, increased consumption of pre-prepared foods compared to home-cooked meals can lead to weight gain, which in turn can lead to obesity. Nevertheless, to date no study has examined this relationship. The purpose of this study is to determine (i) the association between adult body mass index (BMI) and the time spent preparing meals, and (ii) the association between adult BMI and time spent shopping for food. Data on food habits and body size were collected with a self-report survey of ethnically diverse adults between the ages of 17 and 70 at a large university. The survey was used to recruit people to participate in nutrition or appetite studies. Among other data, the survey collected demographic data (gender, race/ethnicity), minutes per week spent in preparing meals and minutes per week spent grocery shopping. Height and weight were self-reported and used to calculate BMI. The study population consisted of 689 subjects, of which 276 were male and 413 were female. The mean age was 23.5 years, with a median age of 21 years. The fraction of subjects with BMI less than 24.9 was 65%, between 25 and 29.9 was 26%, and 30 or greater was 9%. Analysis of variation was used to examine associations between food preparation time and BMI. ^ The results of the study showed that there were no significant statistical association between adult healthy weight, overweight and obesity with either food preparation time and grocery shopping time. Of those in the sample who reported preparing food, the mean food preparation time per week for the healthy weight, overweight, and obese groups were 12.8 minutes, 12.3 minutes, and 11.6 minutes respectively. Similarly, the mean weekly grocery shopping for healthy, overweight, and obese groups were 60.3 minutes per week (8.6min./day), 61.4 minutes (8.8min./day), and 57.3 minutes (8.2min./day), respectively. Since this study was conducted through a University campus, it is assumed that most of the sample was students, and a percentage might have been utilizing meal plans on campus, and thus, would have reported little meal preparation or grocery shopping time. Further research should examine the relationships between meal preparation time and time spent shopping for food in a sample that is more representative of the general public. In addition, most people spent very little time preparing food, and thus, health promotion programs for this population need to focus on strategies for preparing quick meals or eating in restaurants/cafeterias. ^

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A life table methodology was developed which estimates the expected remaining Army service time and the expected remaining Army sick time by years of service for the United States Army population. A measure of illness impact was defined as the ratio of expected remaining Army sick time to the expected remaining Army service time. The variances of the resulting estimators were developed on the basis of current data. The theory of partial and complete competing risks was considered for each type of decrement (death, administrative separation, and medical separation) and for the causes of sick time.^ The methodology was applied to world-wide U.S. Army data for calendar year 1978. A total of 669,493 enlisted personnel and 97,704 officers were reported on active duty as of 30 September 1978. During calendar year 1978, the Army Medical Department reported 114,647 inpatient discharges and 1,767,146 sick days. Although the methodology is completely general with respect to the definition of sick time, only sick time associated with an inpatient episode was considered in this study.^ Since the temporal measure was years of Army service, an age-adjusting process was applied to the life tables for comparative purposes. Analyses were conducted by rank (enlisted and officer), race and sex, and were based on the ratio of expected remaining Army sick time to expected remaining Army service time. Seventeen major diagnostic groups, classified by the Eighth Revision, International Classification of Diseases, Adapted for Use In The United States, were ranked according to their cumulative (across years of service) contribution to expected remaining sick time.^ The study results indicated that enlisted personnel tend to have more expected hospital-associated sick time relative to their expected Army service time than officers. Non-white officers generally have more expected sick time relative to their expected Army service time than white officers. This racial differential was not supported within the enlisted population. Females tend to have more expected sick time relative to their expected Army service time than males. This tendency remained after diagnostic groups 580-629 (Genitourinary System) and 630-678 (Pregnancy and Childbirth) were removed. Problems associated with the circulatory system, digestive system and musculoskeletal system were among the three leading causes of cumulative sick time across years of service. ^