Background Hand-arm vibration (HAV) is an occupational hazard which, cumulatively, leads to hand-arm vibration syndrome (HAVS). Detection and reduction of HAV can help prevent the disease or slow down its progress. Unfortunately, assessment of HAV through direct measurement is difficult due to the high cost of measurement equipment, interruption of work performance, and long travel cost to worksites. An alternative assessment method is through development of an exposure prediction model to identify workplace, tool, and task characteristics which significantly predict HAV exposure. Purpose The purpose of this study is twofold: 1) to determine the extent and nature of previously published scientific journal articles on exposure prediction modeling of HAV through performing a systematic review; and 2) to develop a new exposure prediction model using administrative data to find significant HAV predictors. Methods A systematic review of relevant studies involved humans aged 18 or over, applicable to occupational setting, with vibration measured using tri-axial accelerometer and statistical modeling of the effects of occupational characteristics on measure HAV were identified. Five online bibliographic databases (Medline, CINAHL, Web of Science, Scopus, and EMBASE) were searched using a combined word list of terms under three categories: “occupational diseases”; “Hand-arm vibration”; and “Statistical Modelling”. Two multiple linear regression models predicting average hand-arm vibration exposure over 8 hour day, A(8), were built using enforcement data collected by Ministry of Labour Relations and Workplace Safety Saskatchewan using standard model building method. In addition, GEE was used to account for repeated data collection within workers and worksites. Results In the first manuscript, 7 articles found were deemed relevant. Four studies built regression models, while three used ANOVA to find significant HAV predictors. Examples of significant HAV predictors included: tool age, tool weight, participant, and operating power; the proportion of HAV variance explained varied from 46 to 90%. In the second manuscript, the models based on administrative data explained 16% to 27% of A(8) variance. The included variables were tool power source, vibration control, and job type for the parsimonious model; the comprehensive model has the same variables as parsimonious along with accelerometer attachment method and tool brand. Conclusion HAV prediction through exposure prediction modeling is a relatively new method for assessing occupational HAV. It is feasible to find HAV predictors using low-cost administrative data, and variables such as tool power source, job type, and vibration control make promising predictors. However, the variance explained will be lower than using data collected for research purposes. The significant predictors found in the systematic review can be considered when installing protective measures in the future. The significant predictors found and procedures used from the modeling study can be considered for future HAV exposure prediction modeling studies.

Necrotizing enterocolitis (NEC) is a costly and deadly disease in neonates. Composite risk for NEC is poorly understood and consensus has not been established on the relevance of risk factors. This two-phase study attempted to validate and test a neonatal NEC risk index, GutCheck(NEC). Phase I used an E-Delphi methodology in which experts (n=35) rated the relevance of 64 potential NEC risk factors. Items were retained if they achieved predefined levels of expert consensus or stability. After three rounds, 43 items were retained (CVI=.77). Qualitative analysis revealed two broad themes: individual characteristics of vulnerability and the impact of contextual variation within the NICU on NEC risk. In Phase II, the predictive validity of GutCheck(NEC) was evaluated using a sample from the Pediatrix BabySteps Clinical Data Warehouse (CDW). The sample included infants born<1500 grams, before 36 weeks, and without congenital anomalies or spontaneous intestinal perforation (N=58,818, of which n=35,005 for empiric derivation and n=23,813 for empiric validation). Backward stepwise likelihood-ratio method regression was used to reduce the number of predictive factors in GutCheck(NEC) to 11 and derive empiric weights. Items in the final GutCheck(NEC) were gestational age, history of a transfusion, NICU-specific NEC risk, late onset sepsis, multiple infections, hypotension treated with Inotropic medications, Black or Hispanic race, outborn status, metabolic acidosis, human milk feeding on both day 7 and day 14 (reduces risk) and probiotics (reduces risk).Discrimination was fair in the case-control sample (AUC=.67, 95% CI .61-.73) but better in the validation set (AUC=.76, 95% CI .75-.78) and best for surgical NEC (AUC=.84, 95% CI .82-.84) and infants who died from NEC (AUC=.83, 95% CI .81-.85). A GutCheck(NEC) score of 33 (range 0-58) yielded a sensitivity of .78 and a specificity of .74 in the validation set. Intra-individual reliability was acceptable (ICC (19) =.97, p<.001). Future research is needed to repeat this procedure in infants between 1500 and 2500 grams, complete psychometric testing, and explore unit variation in NEC rates using a comprehensive approach.

Thermal Integrity Profiling (TIP) is a relatively new non-destructive test method for evaluating the post-construction quality of drilled shafts. Therein anomalies in a shaft are indicated by variations in its thermal profile when measured during the curing stages of the concrete. A considerable benefit with this method is in the ability to detect anomalies both inside and outside the reinforcement cage, as well as provide a measure of lateral cage alignment. Similarly remarkable, early developments showed that the shape of a temperature profile (with depth) matched closely with the shape of the shaft, thus allowing for a straightforward interpretation of data. As with any test method, however, the quality of the results depends largely on the level of analysis and the way in which test data is interpreted, which was the focus of this study. This dissertation presents the findings from both field data and computer models to address and improve TIP analysis methods, specifically focusing on: (1) the analysis of non-uniform temperature distributions caused by external boundary conditions, (2) proper selection of temperature-radius relationships, and (3) understanding the effects of time on analysis. Numerical modeling was performed to identify trends in the temperature distributions in drilled shafts during concrete hydration. Specifically, computer generated model data was used to identify the patterns of the non-linear temperature distributions that occur at the ends of a shaft caused by the added heat loss boundary in the longitudinal direction. Similar patterns are observed at locations in a shaft where drastic changes in external boundary conditions exist (e.g. shafts that transition from soil to water or air). Numerical modeling data was also generated to examine the relationship between measured temperatures and shaft size/shape which is a fundamental concept of traditional TIP analysis. A case study involving a shaft from which 24hrs of internal temperature data was investigated and compared to results from a computer generated model made to mimic the field conditions of the shaft. Analysis of field collected and model predicted data was performed to examine the treatment of non-linear temperature distributions at the ends of the shaft and where a mid-shaft change in boundary was encountered. Additionally, the analysis was repeated for data over a wide range of concrete ages to examine the effects of time on the results of analysis. Finally, data from over 200 field tested shafts was collected and analyzed to perform a statistical evaluation of the parameters used for interpretation of the non-linear distributions at the top and bottom of each shaft. This investigation incorporated an iterative algorithm which determined the parameters required to provide a best-fit solution for the top and bottom of each shaft. A collective statistical evaluation of the resulting parameters was then used to better define the proper methods for analyzing end effects. Findings revealed that the effects of non-uniform temperature distributions in drilled shaft thermal profiles can be offset with a curve-fitting algorithm defined by a hyperbolic tangent function that closely matches the observed thermal distribution. Numerical models and statistical evaluations provided a rationale for proper selection of the function defining parameters. Additionally, numerical modeling showed that the true temperature-to-radius relationship in drilled shafts is non-linear, but in most cases a linear approximation is well suited. Finally, analysis of both model and field data showed that concrete age has virtually no effect on the final results of thermal profile analysis, as long as temperature measurements are taken within the dominate stages of concrete hydration.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24889号 / 工博第5169号 / 新制||工||1987(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 米田 稔, 教授 西村 文武, 教授 松井 康人 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM