Evaluation of candidate genes for susceptibility to spina bifida

Neural tube defects (NTDs) are the most common severely disabling birth defects in the United States, with a frequency of approximately 1–2 of every 1,000 births. This text includes the identification and evaluation of candidate susceptibility genes that confer risk for the development of neural tube defects (NTDs). The project focused on isolated meningomyelocele, also termed spina bifida (SB). ^ Spina bifida is a complex disease with multifactorial inheritance, therefore the subject population (consisting of North American Caucasians and Hispanics of Mexicali-American descent) was composed of 459 simplex SB families who were tested for genetic associations utilizing the transmission disequilibrium test (TDT), a nonparametric linkage technique. Three categories of candidate genes were studied, including (1) human equivalents of genes determined in mouse models to cause NTDs, (2) HOX and PAX genes, and (3) the MTHFR gene involved in the metabolic pathway of folate. ^ The C677T variant of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene was the first mutation in this gene to be implicated as a risk factor for NTDs. Our evaluation of the MTHFR gene provides evidence that maternal C677T homozygosity is a risk factor for upper level spina bifida defects in Hispanics [OR = 2.3, P = 0.02]. This observed risk factor is of great importance due to the high prevalence of this homozygous genotype in the Hispanic population. Additionally, maternal C677T/A1298C compound heterozygosity is a risk factor for upper level spina bifida defects in non-Hispanic whites [OR = 3.6, P = 0.03]. ^ For TDT analysis, our total population of 1128 subjects were genotyped for 54 markers from within and/or flanking the 20 candidate genes/gene regions of interest. Significant TDT findings were obtained for 3 of the 54 analyzed markers: d20s101 flanking the PAX1 gene (P = 0.019), d1s228 within the PAX7 gene (P = 0.011), and d2s110 within the PAX8 gene (P = 0.013). These results were followed-up by testing the genes directly for mutations utilizing single-strand conformational analysis (SSCA) and direct sequencing. Multiple variations were detected in each of these PAX genes; however, these variations were not passed from parent to child in phase with the positively transmitted alleles. Therefore, these variations do not contribute to the susceptibility of spina bifida, but rather are previously unreported single nucleotide polymorphisms. ^

Interruptions in Workflow for RNs in a Level One Trauma Center

An understanding of interruptions in healthcare is important for the design, implementation, and evaluation of health information systems and for the management of clinical workflow and medical errors. The purpose of this study is to identify and classify the types of interruptions experienced by Emergency Department(ED) nurses working in a Level One Trauma Center. This was an observational field study of Registered Nurses (RNs) employed in a Level One Trauma Center using the shadowing method. Results of the study indicate that nurses were both recipients and initiators of interruptions. Telephones, pagers, and face-to-face conversations were the most common sources of interruptions. Unlike other industries, the healthcare community has not systematically studied interruptions in clinical settings to determine and weigh the necessity of the interruption against their sometimes negative results such as medical errors, decreased efficiency, and increased costs. Our study presented here is an initial step to understand the nature, causes, and effects of interruptions, thereby improving both the quality of healthcare and patient safety. We developed an ethnographic data collection technique and a data coding method for the capturing and analysis of interruptions. The interruption data we collected are systematic, comprehensive, and close to exhaustive. They confirmed the findings from earlier studies by other researchers that interruptions are frequent events in critical care and other healthcare settings. We are currently using these data to analyze the workflow dynamics of ED clinicians, to identify the bottlenecks of information flow, and to develop interventions to improve the efficiency of emergency care through the management of interruptions.

Interruptions in workflow for RNs in a Level One Trauma Center.

An understanding of interruptions in healthcare is important for the design, implementation, and evaluation of health information systems and for the management of clinical workflow and medical errors. The purpose of this study is to identify and classify the types of interruptions experienced by ED nurses working in a Level One Trauma Center. This was an observational field study of Registered Nurses employed in a Level One Trauma Center using the shadowing method. Results of the study indicate that nurses were both recipients and initiators of interruptions. Telephone, pagers, and face-to-face conversations were the most common sources of interruptions. Unlike other industries, the outcomes caused by interruptions resulting in medical errors, decreased efficiency and increased cost have not been systematically studied in healthcare. Our study presented here is an initial step to understand the nature, causes, and effects of interruptions, and to develop interventions to manage interruptions to improve healthcare quality and patient safety. We developed an ethnographic data collection technique and a data coding method for the capturing and analysis of interruptions. The interruption data we collected are systematic, comprehensive, and close to exhaustive. They confirmed the findings from early studies by other researchers that interruptions are frequent events in critical care and other healthcare settings. We are currently using these data to analyze the workflow dynamics of ED clinicians, identify the bottlenecks of information flow, and develop interventions to improve the efficiency of emergency care through the management of interruptions.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^