6 resultados para trajectories of PLT practitioners

em DigitalCommons@The Texas Medical Center


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Background. Retail clinics, also called convenience care clinics, have become a rapidly growing trend since their initial development in 2000. These clinics are coupled within a larger retail operation and are generally located in "big-box" discount stores such as Wal-mart or Target, grocery stores such as Publix or H-E-B, or in retail pharmacies such as CVS or Walgreen's (Deloitte Center for Health Solutions, 2008). Care is typically provided by nurse practitioners. Research indicates that this new health care delivery system reduces cost, raises quality, and provides a means of access to the uninsured population (e.g., Deloitte Center for Health Solutions, 2008; Convenient Care Association, 2008a, 2008b, 2008c; Hansen-Turton, Miller, Nash, Ryan, Counts, 2007; Salinsky, 2009; Scott, 2006; Ahmed & Fincham, 2010). Some healthcare analysts even suggest that retail clinics offer a feasible solution to the shortage of primary care physicians facing the nation (AHRQ Health Care Innovations Exchange, 2010). ^ The development and performance of retail clinics is heavily dependent upon individual state policies regulating NPs. Texas currently has one of the most highly regulated practice environments for NPs (Stout & Elton, 2007; Hammonds, 2008). In September 2009, Texas passed Senate Bill 532 addressing the scope of practice of nurse practitioners in the convenience care model. In comparison to other states, this law still heavily regulates nurse practitioners. However, little research has been conducted to evaluate the impact of state laws regulating nurse practitioners on the development and performance of retail clinics. ^ Objectives. (1). To describe the potential impact that SB 532 has on retail clinic performance. (2). To discuss the effectiveness, efficiency, and equity of the convenience care model. (3). To describe possible alternatives to Texas' nurse practitioner scope of practice guidelines as delineated in Texas Senate Bill 532. (4). To describe the type of nurse practitioner state regulation (i.e. independent, light, moderate, or heavy) that best promotes the convenience care model. ^ Methods. State regulations governing nurse practitioners can be characterized as independent, light, moderate, and heavy. Four state NP regulatory types and retail clinic performance were compared and contrasted to that of Texas regulations using Dunn and Aday's theoretical models for conducting policy analysis and evaluating healthcare systems. Criteria for measurement included effectiveness, efficiency, and equity. Comparison states were Arizona (Independent), Minnesota (Light), Massachusetts (Moderate), and Florida (Heavy). ^ Results. A comparative states analysis of Texas SB 532 and alternative NP scope of practice guidelines among the four states: Arizona, Florida, Massachusetts, and Minnesota, indicated that SB 532 has minimal potential to affect the shortage of primary care providers in the state. Although SB 532 may increase the number of NPs a physician may supervise, NPs are still heavily restricted in their scope of practice and limited in their ability to act as primary care providers. Arizona's example of independent NP practice provided the best alternative to affect the shortage of PCPs in Texas as evidenced by a lower uninsured rate and less ED visits per 1,000 population. A survey of comparison states suggests that retail clinics thrive in states that more heavily restrict NP scope of practice as opposed to those that are more permissive, with the exception of Arizona. An analysis of effectiveness, efficiency, and equity of the convenience care model indicates that retail clinics perform well in the areas of effectiveness and efficiency; but, fall short in the area of equity. ^ Conclusion. Texas Senate 532 represents an incremental step towards addressing the problem of a shortage of PCPs in the state. A comparative policy analysis of the other four states with varying degrees of NP scope of practice indicate that a more aggressive policy allowing for independent NP practice will be needed to achieve positive changes in health outcomes. Retail clinics pose a temporary solution to the shortage of PCPs and will need to expand their locations to poorer regions and incorporate some chronic care to obtain measurable health outcomes. ^

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Reported is a research study to assess the opinions of family practitioners on the status of families in Oklahoma. Researchers employed the Delphi method to achieve consensus among key informants in the family practice field about the strengths and weaknesses of Oklahoma families, threats facing families in the state, and means to strengthening family life in Oklahoma. The study yielded qualitative data from the key informants, which the researchers then condensed into response categories to feed back to informants to rate. Family practitioners identified resilience, spirituality, and access to support systems as the greatest strengths, and listed substance abuse, poverty, and generational cycles of dysfunction as the greatest weaknesses of Oklahoma families. Recommendations by these practitioners are given for improvements in addressing family needs.

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Introduction: Obesity is an epidemic in the United States, especially among Hispanics and African-Americans. Studies of obesity and breast cancer risk and subtype have been conducted primarily in non-Hispanic whites. Obesity is inversely associated with premenopausal breast cancer, but both obesity and weight gain increase the risk of postmenopausal disease. Obesity has been associated with breast cancer subtype in many studies. Methods: To assess the association between changes in body mass index (BMI) over the lifetime, weight gain, and breast cancer in Mexican-American women, we conducted a case-control study using 149 cases and 330 age-matched controls. In a second study, we identified 212 African-American and 167 Mexican-American women with breast cancer in the ongoing ELLA Bi-National Breast Cancer Study, abstracted medical charts to classify tumors as ER+/PR+, HER2+, or ER-/PR-/HER2-, and assessed the association between lifetime changes in body mass index, weight gain, and breast cancer subtype. In both studies, growth mixture modeling was use to identify trajectories of change in BMI over the lifetime, and these trajectories were used as exposures in a logistic regression model to calculate odds ratios (OR). Results: There was no association between trajectories of change in BMI and breast cancer risk in Mexican-American women. In addition, BMI at ages 15 and 30 and at diagnosis was not associated with breast cancer. However, adult weight gain was inversely associated with breast cancer risk (per 5kg, OR=0.92, 95% CI: 0.85-0.99). The case-only analysis found no association between obesity at ages 15 and 30 and at diagnosis and breast cancer subtype. Further, there was no association between adult weight gain (defined as weight change from age 15 to time of diagnosis) and breast cancer subtype. Conclusions: Obesity was not associated with breast cancer risk in Mexican-American women, while adult weight gain reduced the risk independently of menopausal status. These results are contradictory of those in non-Hispanic white women and suggest that the etiology of breast cancer may differ by race/ethnicity. Further, obesity was not associated with breast cancer subtype in African-American and Mexican-American women, contrary to results in non-Hispanic white women. ^

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Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

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Dietary intake is a complex, health-related behavior, and although individual-level theoretical models explain some variation in dietary intake, comprehensive theoretical models such as the ecological framework describe the multiple levels which influence diet-related behaviors. Thus, the ecological framework is a preferred model for designing comprehensive nutrition interventions. While ecological-based nutrition interventions have been described, little work has focused on interventions in the hospital setting. Because hospitals are considered the hallmarks of health, it might seem that hospitals would regularly engage in worksite nutrition promotion; however, recent publications and other anecdotal evidence have indicated otherwise. The first paper of this dissertation systematically reviewed the scientific literature between 1996 and 2012 and identified 13 outcome evaluation trials for hospital-based worksite nutrition interventions. Of these 13 interventions, only one intervention targeted three of the four levels of the ecological framework and no intervention targeted all four levels. Only half of the interventions targeted the physical environment of hospitals, thus warranting more investigation into this specific level of the ecological framework in this setting. ^ A critical type of nutrition-related physical environments is the consumer nutrition environment. Although other tools measure the consumer nutrition environments of stores and restaurants, no tool specifically measured the consumer nutrition environments of hospitals until the CDC developed the Healthy Hospital Environment Scan for Cafeterias, Vending Machines, and Gift Shops (HHES-CVG). The HHES-CVG, a tool which measures the consumer nutrition environments of hospital cafeterias, vending machines, and gifts shops, was released in November 2011, and in the second paper of this dissertation, the reliability of this tool was investigated. Two trained raters visited 39 hospitals across Southern California between February and May 2012, and based on analyses of the raters' findings, the HHES-CVG exhibited strong reliability metrics (inter-observer agreement between 74 and 100%, and an intraclass correlation coefficient of 0.961 for the overall nutrition composite score). Because the HHES-CVG was found to be a reliable tool, the third paper of this dissertation presented HHES-CVG results from the 39 hospitals. Overall, hospitals only scored about one-fourth of the total possible points for the nutrition composite score, indicating that most facilities do not have acceptable consumer nutrition environments. Some of the best practices observed in cafeterias were significantly associated with having a large facility and with having a contracted foodservice operation, but overall nutrition composite score was not associated with any specific facility or operation type. ^ The dissertation concluded that much work is needed in order to improve the consumer nutrition environments of hospitals. Practitioners and healthcare administrators should consider starting with ecological-based interventions addressing all levels including the physical environment.^

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Left ventricular mass (LVM) is a strong predictor of cardiovascular disease (CVD) in adults. However, normal growth of LVM in healthy children is not well understood, and previous results on independent effects of body size and body fatness on LVM have been inconsistent. The purpose of this study was (1) to establish the normal growth curve of LVM from age 8 to age 18, and evaluate the determinants of change in LVM with age, and (2) to assess the independent effects of body size and body fatness on LVM.^ In Project HeartBeat!, 678 healthy children aged 8, 11 and 14 years at baseline were enrolled and examined at 4-monthly intervals for up to 4 years. A synthetic cohort with continuous observations from age 8 to 18 years was constructed. A total of 4608 LVM measurements was made from M-mode echocardiography. The multilevel linear model was used for analysis.^ Sex-specific trajectories of normal growth of LVM from age 8 to 18 was displayed. On average, LVM was 15 g higher in males than females. Average LVM increased linearly in males from 78 g at age 8 to 145 g at age 18. For females, the trajectory was curvilinear, nearly constant after age 14. No significant racial differences were found. After adjustment for the effects of body size and body fatness, average LVM decreased slightly from age 8 to 18, and sex differences in changes of LVM remained constant.^ The impact of body size on LVM was examined by adding to a basic LVM-sex-age model one of 9 body size indicators. The impact of body fatness was tested by further introducing into each of the 9 LVM models (with one or another of the body size indicators) one of 4 body fatness indicators, yielding 36 models with different body size and body fatness combinations. The results indicated that effects of body size on LVM can be distinguished between fat-free body mass and fat body mass, both being independent, positive predictors. The former is the stronger determinant. When a non-fat-free body size indicator is used as predictor, the estimated residual effect of body fatness on LVM becomes negative. ^