14 resultados para predictive compensation

em DigitalCommons@The Texas Medical Center


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Li-Fraumeni Syndrome (LFS) is a hereditary cancer syndrome which predisposes individuals to cancer beginning in childhood. These risks are spread across a lifetime, from early childhood to adulthood. Mutations in the p53 tumor suppressor gene are known to cause the majority of cases of LFS. The risk for early onset cancer in individuals with Li-Fraumeni Syndrome is high. Studies have shown that individuals with LFS have a 90% lifetime cancer risk. Children under 18 have up to a 15% chance of cancer development. Effectiveness of cancer screening and management in individuals with Li-Fraumeni Syndrome is unclear. Screening for LFS-associated cancers has not been shown to reduce mortality. Due to the lack of effective screening techniques for childhood cancers, institutions vary with regard to their policies on testing children for LFS. There are currently no national guidelines regarding predictive testing of children who are at risk of inheriting LFS. No studies have looked at parental attitudes towards predictive p53 genetic testing in their children. This was a cross-sectional pilot study aimed at describing these attitudes. We identified individuals whose children were at risk for inheriting p53 genetic mutations. These individuals were provided with surveys which included validated measures addressing attitudes and beliefs towards genetic testing. The questionnaire included qualitative and quantitative measures. Six individuals completed and returned the questionnaire with a response rate of 28.57%. In general, respondents agreed that parents should have the opportunity to obtain p53 genetic testing for their child. Parents vary in regard to their attitudes towards who should be involved in the decision making process and at what time and under what considerations testing should occur. Testing motivations cited most important by respondents included family history, planning for the future and health management. Concern for insurance genetic discrimination was cited as the most important “con” to genetic testing. Although limited by a poor response rate, this study can give health care practitioners insight into testing attitudes and beliefs of families considering pediatric genetic testing.

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Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

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The increased use of vancomycin in hospitals has resulted in a standard practice to monitor serum vancomycin levels because of possible nephrotoxicity. However, the routine monitoring of vancomycin serum concentration is under criticism and the cost effectiveness of such routine monitoring is in question because frequent monitoring neither results in increase efficacy nor decrease nephrotoxicity. The purpose of the present study is to determine factors that may place patients at increased risk of developing vancomycin induced nephrotoxicity and for whom monitoring may be most beneficial.^ From September to December 1992, 752 consecutive in patients at The University of Texas M. D. Anderson Cancer Center, Houston, were prospectively evaluated for nephrotoxicity in order to describe predictive risk factors for developing vancomycin related nephrotoxicity. Ninety-five patients (13 percent) developed nephrotoxicity. A total of 299 patients (40 percent) were considered monitored (vancomycin serum levels determined during the course of therapy), and 346 patients (46 percent) were receiving concurrent moderate to highly nephrotoxic drugs.^ Factors that were found to be significantly associated with nephrotoxicity in univariate analysis were: gender, base serum creatinine greater than 1.5mg/dl, monitor, leukemia, concurrent moderate to highly nephrotoxic drugs, and APACHE III scores of 40 or more. Significant factors in the univariate analysis were then entered into a stepwise logistic regression analysis to determine independent predictive risk factors for vancomycin induced nephrotoxicity.^ Factors, with their corresponding odds ratios and 95% confidence limits, selected by stepwise logistic regression analysis to be predictive of vancomycin induced nephrotoxicity were: Concurrent therapy with moderate to highly nephrotoxic drugs (2.89; 1.76-4.74), APACHE III scores of 40 or more (1.98; 1.16-3.38), and male gender (1.98; 1.04-2.71).^ Subgroup (monitor and non-monitor) analysis showed that male (OR = 1.87; 95% CI = 1.01, 3.45) and moderate to highly nephrotoxic drugs (OR = 4.58; 95% CI = 2.11, 9.94) were significant for nephrotoxicity in monitored patients. However, only APACHE III score (OR = 2.67; 95% CI = 1.13,6.29) was significant for nephrotoxicity in non-monitored patients.^ The conclusion drawn from this study is that not every patient receiving vancomycin therapy needs frequent monitoring of vancomycin serum levels. Such routine monitoring may be appropriate in patients with one or more of the identified risk factors and low risk patients do not need to be subjected to the discomfort and added cost of multiple blood sampling. Such prudent selection of patients to monitor may decrease cost to patients and hospital. ^

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A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^

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With substance abuse treatment expanding in prisons and jails, understanding how behavior change interacts with a restricted setting becomes more essential. The Transtheoretical Model (TTM) has been used to understand intentional behavior change in unrestricted settings, however, evidence indicates restrictive settings can affect the measurement and structure of the TTM constructs. The present study examined data from problem drinkers at baseline and end-of-treatment from three studies: (1) Project CARE (n = 187) recruited inmates from a large county jail; (2) Project Check-In (n = 116) recruited inmates from a state prison; (3) Project MATCH, a large multi-site alcohol study had two recruitment arms, aftercare (n = 724 pre-treatment and 650 post-treatment) and outpatient (n = 912 pre-treatment and 844 post-treatment). The analyses were conducted using cross-sectional data to test for non-invariance of measures of the TTM constructs: readiness, confidence, temptation, and processes of change (Structural Equation Modeling, SEM) across restricted and unrestricted settings. Two restricted (jail and aftercare) and one unrestricted group (outpatient) entering treatment and one restricted (prison) and two unrestricted groups (aftercare and outpatient) at end-of-treatment were contrasted. In addition TTM end-of-treatment profiles were tested as predictors of 12 month drinking outcomes (Profile Analysis). Although SEM did not indicate structural differences in the overall TTM construct model across setting types, there were factor structure differences on the confidence and temptation constructs at pre-treatment and in the factor structure of the behavioral processes at the end-of-treatment. For pre-treatment temptation and confidence, differences were found in the social situations factor loadings and in the variance for the confidence and temptation latent factors. For the end-of-treatment behavioral processes, differences across the restricted and unrestricted settings were identified in the counter-conditioning and stimulus control factor loadings. The TTM end-of-treatment profiles were not predictive of drinking outcomes in the prison sample. Both pre and post-treatment differences in structure across setting types involved constructs operationalized with behaviors that are limited for those in restricted settings. These studies suggest the TTM is a viable model for explicating addictive behavior change in restricted settings but calls for modification of subscale items that refer to specific behaviors and caution in interpreting the mean differences across setting types for problem drinkers. ^

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The human cytochrome P450 3A (CYP3A) subfamily is responsible for most of the metabolism of therapeutic drugs; however, an adequate in vivo model has yet to be discovered. This study begins with an investigation of a controversial topic surrounding the human CYP3As--estrogen regulation. A novel approach to this topic was used by defining expression in the estrogen-responsive endometrium. This study shows that estrogen down-regulates CYP3A4 expression in the endometrium. On the other hand, analogous studies showed an increase in CYP3A expression as age increases in liver tissue. Following the discussion of estrogen regulation, is an investigation of the cross-species relationships among all of the CYP3As was completed. The study compares isoforms from piscines, avians, rodents, canines, ovines, bovines, and primates. Using the traditional phylogenetic analyses and employing a novel approach using exon and intron lengths, the results show that only another primate could be the best animal model for analysis of the regulation of the expression of the human CYP3As. This analysis also demonstrated that the chimpanzee seems to be the best available human model. Moreover, the study showed the presence and similarities of one additional isoform in the chimpanzee genome that is absent in humans. Based on these results, initial characterization of the chimpanzee CYP3A subfamily was begun. While the human genome contains four isoforms--CYP3A4, CYP3A5, CYP3A7, and CYP3A43--the chimpanzee genome has five, the four previously mentioned and CYP3A67. Both species express CYP3A4, CYP3A5, and CYP3A43, but humans express CYP3A7 while chimpanzees express CYP3A67. In humans, CYP3A4 is expressed at higher levels than the other isoforms, but some chimpanzee individuals express CYP3A67 at higher levels than CYP3A4. Such a difference is expected to alter significantly the total CYP3A metabolism. On the other hand, any study considering individual isoforms would still constitute a valid method of study for the human CYP3A4, CYP3A5, and CYP3A43 isoforms. ^

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The purpose of this study was to examine and describe the changes in physician provider workforce, before and after two regulatory changes were implemented by the Texas Workers' Compensation Commission (TWCC) in August and September of 2003: Fee schedules and the Approved Doctor's List (ADL). The number and type of physicians who participated in the program after the changes went into effect were measured and compared to projections based on natural attrition. In addition, interviews with key stakeholders were conducted regarding the program changes. ^ Collectively, this evidence suggests that physician response followed the same patterns as shown in previous research. The number of physicians who continued to participate and bill the Texas workers' compensation program decreased significantly as a result of the regulatory changes. The consequences of these changes on access and quality of care need to be documented with empirical research. The availability of physicians in the workforce is linked to access to care. The type and location of physicians who remained in the system also have impact on quality and access to care. ^

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The use of feminine products such as vaginal douches, tampons, and sanitary napkins are common among women. Despite the results of some studies that suggest an association between douching and bacterial vaginosis, douching remains a topic that is understudied. The possibility of an association between tampon use and infection has not been significantly investigated since the toxic shock outbreak in the 1980s. The first objective of our study was to evaluate demographic, reproductive health, and sexual behavior variables to establish an epidemiologic profile of menstruating women who reported douching and women who reported using sanitary napkins only. The second objective of our study was to evaluate whether the behaviors of douching and using tampons were associated with an increased risk of bacterial vaginosis or trichomonas. We analyzed these factors, using logistic regression, among the 3,174 women from the NHANES cross sectional data from 2001-2004, who met the inclusion criteria determined for our study. We established an epidemiologic profile for women who had the highest frequency of douching reported as women who were age 36-49, had a high school education or GED, black race, not taking oral contraceptives, reported vaginal symptoms in the last month, two or more sexual partners in the last year, or tested positive for bacterial vaginosis or trichomonas. The profile for those who had the highest frequency of exclusive sanitary napkin use included women with less than a high school education, married women, women classified as black or "other" in race, and women who were not on oral contraceptives. While we were able to establish a significant increase in the odds of douching among women who tested positive for bacterial vaginosis or trichomonas, we did not find any significant difference in the odds of exclusive napkin use and testing negative for bacterial vaginosis or trichomonas.^

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The potential for significant human populations to experience long-term inhalation of formaldehyde and reports of symptomatology due to this exposure has led to a considerable interest in the toxicologic assessment of risk from subchronic formaldehyde exposures using animal models. Since formaldehyde inhalation depresses certain respiratory parameters in addition to its other forms of toxicity, there is a potential for the alteration of the actual dose received by the exposed individual (and the resulting toxicity) due to this respiratory effect. The respiratory responses to formaldehyde inhalation and the subsequent pattern of deposition were therefore investigated in animals that had received subchronic exposure to the compound, and the potential for changes in the formaldehyde dose received due to long-term inhalation evaluated. Male Sprague-Dawley rats were exposed to either 0, 0.5, 3, or 15 ppm formaldehyde for 6 hours/day, 5 days/week for up to 6 months. The patterns of respiratory response, deposition and the compensation mechanisms involved were then determined in a series of formaldehyde test challenges to both the upper and to the lower respiratory tracts in separate groups of subchronically exposed animals and age-specific controls (four concentration groups, two time points). In both the control and pre-exposed animals, there was a characteristic recovery of respiratory parameters initially depressed by formaldehyde inhalation to at or approaching pre-exposure levels within 10 minutes of the initiation of exposure. Also, formaldehyde deposition was found to remain very high in the upper and lower tracts after long-term exposure. Therefore, there was probably little subsequent effect on the dose received by the exposed individual that was attributable to the repeated exposures. There was a diminished initial minute volume response in test challenges of both the upper and lower tracts of animals that had received at least 16 weeks of exposure to 15 ppm, with compensatory increases in tidal volume in the upper tract and respiratory rate in the lower tract. However, this dose-related effect was probably not relevant to human risk estimation because this formaldehyde dose is in excess of that experienced by human populations. ^

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Multiple Endocrine Neoplasia type 1 (MEN1) is a hereditary cancer syndrome characterized by tumors of the endocrine system. Tumors most commonly develop in the parathyroid glands, pituitary gland, and the gastro-entero pancreatic tract. MEN1 is a highly penetrant condition and age of onset is variable. Most patients are diagnosed in early adulthood; however, rare cases of MEN1 present in early childhood. Expert consensus opinion is that predictive genetic testing should be offered at age 5 years, however there are no evidence-based studies that clearly establish that predictive genetic testing at this age would be beneficial since most symptoms do not present until later in life. This study was designed to explore attitudes about the most appropriate age for predictive genetic testing from individuals at risk of having a child with MEN1. Participants who had an MEN1 mutation were invited to complete a survey and were asked to invite their spouses to participate as well. The survey included several validated measures designed to assess participants’ attitudes about predictive testing in minors. Fifty-eight affected participants and twenty-two spouses/partners completed the survey. Most participants felt that MEN1 genetic testing was appropriate in healthy minors. Younger age and increased knowledge of MEN1 genetics and inheritance predicted genetic testing at a younger age. Additionally, participants who saw more positive than negative general outcomes from genetic testing were more likely to favor genetic testing at younger ages. Overall, participants felt genetic testing should be offered at a younger age than most adult onset conditions and most felt the appropriate time for testing was when a child could understand and participate in the testing process. Psychological concerns seemed to be the primary focus of participants who favored later ages for genetic testing, while medical benefits were more commonly cited for younger age. This exploratory study has implications for counseling patients whose children are at risk of developing MEN1 and illustrates issues that are important to patients and their spouses when considering testing in children.

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The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

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Li- Fraumeni Syndrome (LFS) is a rare autosomal dominant hereditary cancer syndrome caused by mutations in the TP53 gene that predisposes individuals to a wide variety of cancers, including breast cancer, soft tissue sarcomas, osteosarcomas, brain tumors, and adrenocortical carcinomas. Individuals found to carry germline mutations in TP53 have a 90% lifetime cancer risk, with a 20% chance to develop cancer under the age of 20. Despite the significant risk of childhood cancer, predictive testing for unaffected minors at risk for LFS historically has not been recommended, largely due to the lack of available and effective screening for the types of cancers involved. A recently developed screening protocol suggests an advantage to identifying and screening children at risk for LFS and we therefore hypothesized that this alongside with the availability of new screening modalities may substantiate a shift in recommendations for predictive genetic testing in minors at risk for LFS. We aimed to describe current screening recommendations that genetic counselors provide to this population as well as explore factors that may have influenced genetic counselors attitude and practice in regards to this issue. An online survey was emailed to members of the National Society of Genetic Counselors (NSGC) and the Canadian Association of Genetic Counsellors (CAGC). Of an estimated 1000 eligible participants, 172 completed surveys that were analyzed. Genetic counselors in this study were more likely to support predictive genetic testing for this population as the minor aged (p

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Existing data, collected from 1st-year students enrolled in a major Health Science Community College in the south central United States, for Fall 2010, Spring 2011, Fall 2011 and Spring 2012 semesters as part of the "Online Navigational Assessment Vehicle, Intervention Guidance, and Targeting of Risks (NAVIGATOR) for Undergraduate Minority Student Success" with CPHS approval number HSC-GEN-07-0158, was used for this thesis. The Personal Background and Preparation Survey (PBPS) and a two-question risk self-assessment subscale were administered to students during their 1st-year orientation. The PBPS total risk score, risk self-assessment total and overall scores, and Under Representative Minority Student (URMS) status were recorded. The purpose of this study is to evaluate and report the predictive validity of the indicators identified above for Adverse Academic Status Events (AASE) and Nonadvancement Adverse Academic Status Events (NAASE) as well as the effectiveness of interventions targeted using the PBPS among a diverse population of health science community college students. The predictive validity of the PBPS for AASE has previously been demonstrated among health science professions and graduate students (Johnson, Johnson, Kim, & McKee, 2009a; Johnson, Johnson, McKee, & Kim, 2009b). Data will be analyzed using binary logistic regression and correlation using SPSS 19 statistical package. Independent variables will include baseline- versus intervention-year treatments, PBPS, risk self-assessment, and URMS status. The dependent variables will be binary AASE and NAASE status. ^ The PBPS was the first reliable diagnostic and prescriptive instrument to establish documented predictive validity for student Adverse Academic Status Events (AASE) among students attending health science professional schools. These results extend the documented validity for the PBPS in predicting AASE to a health science community college student population. Results further demonstrated that interventions introduced using the PBPS were followed by approximately one-third reduction in the odds of Nonadvancement Adverse Academic Status Events (NAASE), controlling for URMS status and risk self-assessment scores. These results indicate interventions introduced using the PBPS may have potential to reduce AASE or attrition among URMS and nonURMS attending health science community colleges on a broader scale; positively impacting costs, shortages, and diversity of health science professionals.^

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Objective::Describe and understand regional differences and associated multilevel factors (patient, provider and regional) to inappropriate utilization of advance imaging tests in the privately insured population of Texas. Methods: We analyzed Blue Cross Blue Shield of Texas claims dataset to study the advance imaging utilization during 2008-2010 in the PPO/PPO+ plans. We used three of CMS "Hospital Outpatient Quality Reporting" imaging efficiency measures. These included ordering MRI for low back pain without prior conservative management (OP-8) and utilization of combined with and without contrast abdominal CT (OP-10) and thorax CT (OP-11). Means and variation by hospital referral regions (HRR) in Texas were measured and a multilevel logistic regression for being a provider with high values for any the three OP measures was used in the analysis. We also analyzed OP-8 at the individual level. A multilevel logistic regression was used to identify predictive factors for having an inappropriate MRI for low back pain. Results: Mean OP-8 for Texas providers was 37.89%, OP-10 was 29.94% and OP-11 was 9.24%. Variation was higher for CT measure. And certain HRRs were consistently above the mean. Hospital providers had higher odds of high OP-8 values (OP-8: OR, 1.34; CI, 1.12-1.60) but had smaller odds of having high OP-10 and OP-11 values (OP-10: OR, 0.15; CI, 0.12-0.18; OP-11: OR, 0.43; CI, 0.34-0.53). Providers with the highest volume of imaging studies performed, were less likely to have high OP-8 measures (OP-8: OR, 0.58; CI, 0.48-0.70) but more likely to perform combined thoracic CT scans (OP-11: OR, 1.62; CI, 1.34-1.95). Males had higher odds of inappropriate MRI (OR, 1.21; CI, 1.16-1.26). Pattern of care in the six months prior to the MRI event was significantly associated with having an inappropriate MRI. Conclusion::We identified a significant variation in advance imaging utilization across Texas. Type of facility was associated with measure performance, but the associations differ according to the type of study. Last, certain individual characteristics such as gender, age and pattern of care were found to be predictors of inappropriate MRIs.^