Dissertations / Theses on the topic 'Predictive exposure models'

L’exposition aux pesticides augmente le risque de certaines pathologies à long-terme chez les agriculteurs. La prévention repose actuellement sur l’application de « bonnes pratiques agricoles », mal définies et éloignées des pratiques habituelles des agriculteurs. Notre objectif était de contribuer à la prévention en France, en adoptant une double approche systémique et individuelle, visant à améliorer la prise en compte des expositions réelles des agriculteurs et proposer des solutions adaptées.Le premier volet de la thèse a étudié le caractère conservateur des modèles de prédiction des expositions professionnelles en comparant les expositions mesurées dans des divers contextes de travail habituel avec celles calculées par les modèles. Les modèles réglementaires sous-estiment les expositions des opérateurs agricoles, notamment en arboriculture, espaces verts et grandes cultures, en surestimant l’efficacité des équipements de protection individuelle et en négligeant certains déterminants d’exposition. Pour les travailleurs en réentrée/récolte, l’exposition après plusieurs jours est également sous-estimée.Dans le second volet, après un diagnostic des pratiques de prévention des agriculteurs, une intervention multicomposante basée notamment sur des théories psychosociales et destinées à influencer les comportements, alternative à la formation Certiphyto classique, a été développée. Les agriculteurs n’adoptent pas toujours les pratiques de prévention malgré leur connaissance des risques, en raison de barrières, normes sociales et auto-efficacité perçues. Une intervention a été conçue, incluant des démonstrations pratiques, un formateur pair et des processus d’engagement et de changement de normes sociales. Son efficacité sera évaluée par des critères objectifs (expositions urinaires) et auto-rapportés (comportements, perceptions psychosociales).Cette thèse propose d’intégrer davantage les expositions réelles des agriculteurs dans la prévention, tant dans les processus réglementaires que dans la formation pour encourager l’adoption de pratiques de protection. Nous devons poursuivre nos efforts pour atteindre d’autres travailleurs fortement exposés mais peu formés et adopter une approche pluridisciplinaire et globale pour réduire les risques des pesticides
Pesticide exposure increases the risk of some long-term disease among farmers. Prevention currently relies on the application of “good agricultural practices”, which are poorly defined and far from farmers’ usual practices. Our aim was to contribute to prevention in France, by adopting a dual systemic and individual approach, aimed at improving consideration of farmers’ actual exposures and proposing appropriate solutions.The first part of this thesis studied the conservative approach of occupational exposure prediction models, by comparing exposures measured in various usual working contexts with those calculated by the models. Regulatory models underestimate the exposure of agricultural operators, particularly in fruit growing, green spaces and field crops, by overestimating the effectiveness of personal protective equipment and neglecting some exposure determinants. For re-entry/harvest workers, exposure after several days is also underestimated.In the second part, following a diagnosis of farmers’ preventive practices, a multi-component intervention was developed, based in particular on psychosocial theories and designed to influence behavior, as an alternative to standard Certiphyto training. Farmers do not always adopt preventive practices despite their knowledge of the risks, due to perceived barriers, social norms and self-efficacy. An intervention has been designed, including practical demonstrations, a peer trainer and processes of commitment and social norm change. Its effectiveness will be assessed by objective (urinary exposures) and self-reported (behaviours, psychosocial perceptions) criteria.This thesis proposes to integrate farmers’ actual exposures more closely into prevention, both in regulatoryprocesses and in training to encourage the adoption of protective practices. We need to continue our efforts to reach other highly exposed but poorly trained workers, and adopt a multidisciplinary and comprehensive approach to reducing pesticide risks

Introduction: Exposure assessment is a key challenge in environmental epidemiology. When modeling exposures for populations, one should consider (1) the applicability of the exposure model to the health effect of interest (i.e. chronic, acute), (2) the applicability of the model to the population of interest, (3) the extent to which modeled exposures account for individual factors and (4) the sources of variability within the model. Epidemiological studies of traffic-related air pollution and birth outcomes have used a variety of exposure models to estimate exposures for pregnant women. These models are rarely evaluated, let alone specifically for pregnant women. Methods: Measured and modeled personal exposures to air pollutants (nitric oxide: NO, nitrogen dioxide: N0₂ , filter absorbance and fine particles: PM₂․₅) were obtained for 62 pregnant women from 2005-2006 in Vancouver, Canada. Exposures were measured for 48-hours, 1-3 times over the pregnancy. Mobility was assessed using Global Positioning System monitoring and self-reported activity logs; individual factors (dwelling characteristics, socio-economic factors) were assessed using questionnaires. Results: Modeled home concentrations using a traffic-based land-use regression model were moderately predictive of personal samples for NO only (Pearson's r=0.49). Models for NO including home and work locations explained more between subject variance than using home only (4% home only, 2 0 % with home and work). Modeled exposures using ambient monitoring stations were predictive of personal samples for NO (Pearson's r=0.54), absorbance (r=0.29) and PM₂․₅ (r=0.12) mainly due to temporal correlations (within subject variance: NO = 37% , absorbance = 11%, PM, 5 = 9%). Home gas stove was an important determinant of personal exposure for all pollutants. There was a significant (1 hour/day/trimester) increase in time spent at home with increased trimester of pregnancy. Conclusions: In this evaluation, based upon repeated 48-hour exposure measurements, models currently used in air pollution studies were moderately reflective of personal exposures, depending on the specific pollutant and model. Land-use regression shows promise for capturing spatial variability, especially when including mobility (work or school locations) in exposures, whereas monitor-based models are better for capturing temporal variability. Future models should include mobility, where possible, and consider the implications of increasing time at home over pregnancy in assessing exposures for pregnant women.
Medicine, Faculty of
Population and Public Health (SPPH), School of
Graduate

Epidemiological studies have documented significant relationships between outdoor particle exposure and adverse health effects whilst indoor residential PM2.5 exposure is found to be the most influential on total PM2.5 exposure as people spend more than 80% of their time indoors. Accurate exposure assessments for ambient and indoor environments are therefore of equal importance. In this dissertation, we aim to develop methodologies that enhance our ability to quantify fine particulate matter (PM2.5) exposure in both macro and micro perspective. With advanced remote sensing technologies becoming more prevalent and less expensive, there is great potential in employing satellite date to analyze and illustrate ambient air quality in real-time over large geographic areas. In Chapter 1, we introduced a top down approach to construct PM2.5 emission inventory through the integration of mass balance and satellite retrieved daily Aerosol Optical Depth (AOD) at 1km x 1km resolution. The satellite-based inventory provides spatially- and temporally-resolved emission estimates as opposed to the conventional source-oriented inventory that has time lag issues with limited spatial variability to the extent of its data source (usually ground monitoring network). Subsequently in Chapter 2, we quantified the temporal and spatial trends of PM2.5 emission in the North East U.S. using the satellite-based emission inventory. Satellite-based emission trends are in agreement with that of the source-oriented inventories released by the US EPA, showing major reductions achieved in urban areas as well as along important traffic corridors. The technique of this part of the study can be applied to nation-wide or global remote sensing data for estimating PM2.5 emissions and hence improving the quantification of fine particles effects on climate, air quality and human health. While big data may be the game changer for resolving ambient air quality problems, we still face the challenge of data scarcity in microenvironments. In Chapter 3, prediction models that utilize few available samples to assess indoor PM2.5 exposure were developed. We estimated infiltration rate of ambient particles penetrating indoors using sulfur as the tracer element at 95 residences in the Greater Boston Area. Mixed effects model was employed in order to predict infiltration for individual residences. We then estimate indoor levels of PM2.5 and its black carbon (BC) content using outdoor measurements and the estimated infiltration factor. We cross validated the aforementioned models to evaluate their predictive power specifically at dates without indoor information. Cross validation results of the infiltration model (R2=0.89) indicates that mixed effects captured infiltration rates for individual households adequately. We also found strong predictability when sulfur infiltration surrogate and outdoor measurements of PM2.5 and BC were used in predicting indoor exposure levels (R2= 0.79 [PM2.5], 0.76 [BC]). Altogether, the methodologies introduced in this dissertation may serve as frameworks to (1) quantify and illustrate ambient emission of PM2.5 or other pollutants in a macro perspective and (2) determine the relationships between outdoor and indoor air quality and to predict indoor air pollution which are critical information for developing solutions of micro-level air quality problems.

Personalised model generated risk predictions that incorporate several risk factors may motivate people to increase sun protection. The aim of the thesis is to evaluate and build the quality of the evidence for melanoma risk prediction models, contribute to knowledge of melanoma risk factors for inclusion in risk prediction models, and evaluate their effectiveness as preventive tools inclinical practice. Chapter 1 presents an overview of the epidemiology and role of melanoma risk prediction models in prevention. Chapter 2 presents the results of a systematic review of melanoma risk prediction models. The systematic review identified 28 melanoma risk prediction models. However, there was limited reporting of model development and performance measures, and few studies were externally validated or prospectively evaluated in clinical settings. Chapter 3 evaluates occupational sun exposure and melanoma risk to improve understanding of whether this risk factor should be considered for inclusion in risk prediction models by use of two population based case control studies. There was no association between occupational sun exposure and melanoma risk overall or according to anatomical site. Chapters 4 and 5 presents the development and validation of two melanoma risk prediction models, one using self assessed risk factors and the other using clinicallyassessed risk factors. Chapter 6 presents the results of a pragmatic randomised controlled trial, in which 272 Australian general practice patients were randomly allocated to receive (1) real time personalised model generated risk predictions based on self assessed risk factors and tailored prevention advice, or (2) generic prevention advice. There were no statistically significant differences between intervention and control patients in sun protection practices (p=0.13). However average risk patients in the intervention group appeared to show greater sun protection at 6 weeks (mean difference=0.23, on a scale of 1 to 5; 95% confidence interval: 0.01 to 0.45; p=0.04). This thesis adds high quality evidence relevant to the prevention of me lanoma from the development and validation of model generated risk predictions to their implementation and efficacy in clinical practice and is likely to have an impact on preventative care in Australia and internationally.

Transfer of drugs into milk and the clearance of drugs in neonates are critical determinants of the exposure of infants to drugs in breast milk. Models predicting both parameters have been proposed. The objective of this dissertation is to test two models predicting milk to serum ratio and an ontogeny clearance model predicting clearance in the neonate. Predicted milk to serum ratio (M/S) values were generated according to the Atkinson and Begg model. The model did not adequately predict M/S when comparing the predicted values to observed values in the literature. The Fleishaker model was also tested. The model was able to predict whether the drugs appeared in milk by passive diffusion only or whether active transport processes were involved. This model, together with appropriate animal models, is useful in understanding the mechanism of drug transfer into milk. An ontogeny model that predicts clearance was proposed earlier by our laboratory. In order to test the model prediction and assumptions of constant microsomal protein and constant Km for an enzyme-substrate system with age, the male rat was used as an animal model. The ontogeny of Cyp3a1, Cyp3a2, Mdr1a and Mdr1b mRNA was examined in the male rat liver and intestine. The ontogeny pattern of Cyp3a2 mRNA, protein and in vitro Cyp3a activity were found to be similar in male rat liver. The microsomal protein content was found to vary with age in the liver. Km was found to be constant with age for the midazolam 4-hydroxylation by male rat liver microsomes. Scaling factors that extrapolate adult clearance to infant clearance were calculated from in vitro data. The model did not predict the in vivo oral clearance of midazolam for day 7 and 21 age groups from the 112 day age group (adult). The assumption that intestinal availability in the rat pups and adults was equal to unity might not be true resulting in overprediction of rat pup clearance when compared to the adult. Intestinal first pass effect for midazolam in adult rats might be significant. More experiments are needed to further test the model adequacy in clearance prediction.

Nanoparticles (particles less than 100 nm in at least one dimension) exhibit chemical properties that differ from their bulk counterparts. Furthermore, they exhibit increased potential for systemic toxicities due to their deposition deep within pulmonary tissue upon inhalation. Thus, standard regulatory assays alone may not always be appropriate for evaluation of their full spectrum of toxicity. Systems biology (e.g., the study of molecular processes to describe a system as a whole) has emerged as a powerful platform proposed to provide insight in potential hazard, mode of action and human disease relevance. This work makes use of systems biology to characterize carbon black nanoparticle-induced toxicities in pulmonary and extra-pulmonary tissues (i.e., liver and heart) in mice over dose and time. This includes investigations of gene expression profiles, microRNA expression profiles, tissue-specific phenotypes and plasma proteins. The data are discussed in the context of potential use in human health risk assessment. In general, the work provides an example of how toxicogenomics can be used to support human health risk assessment.

博士
國立陽明大學
公共衛生研究所
92
Schizophrenia, with 0.3-0.5% life prevalence in Taiwan, usually deteriorates cognitive function of patients in its chronic natural history of the disease. Schizophrenic patients not only have an increased risk of morbidity and mortality from different physical illness and accidents, but also are minor to acquire general medical cares. Furthermore, most patients received long term treatment of conventional and atypical antipsychotics, and up to 50% of them had significant weight gain problem. Weight gain will increase the health risk, impair of quality of life, and lead to noncompliance, even relapse. The prevention and management of health risk factors resulting from schizophrenia itself or from antipsychotics treatment are essential in caring for schizophrenic patients. However, the degree of weight gain may depend on individual vulnerability, personal behaviors, and environmental factors. The aim of this study is to establish a predictive model of body weight gain in antipsychotics-treated schizophrenic patients. This dissertation try to elucidate prediction of clinical outcomes can be predicted based on algorithms with an acceptable coverage of variance we are interested in. The weight gain due to antipsychotics exposure was chosen as the main clinical outcome in this dissertation by two different forms: dichotomous and continuous data type. Two hundred chronic schizophrenic patients were enrolled with at least 6 months hospitalization while approaching from Yu-Li Veterans Hospital (YLVH) and Tao-Yuan Psychiatric Center (TYPC). The dichotomous outcome for weight gain was predicted by the logistic regression model which was established from 67 schizophrenic patients recruited from YLVH. The reliability of this prediction algorithm is warranted by good sensitivity (90%) and specificity (83%). Two hundred thirty schizophrenic patients participating in TYPC were utilized to establish the linear regression model and to test its accuracy of weight gain prediction, which reached 92% compared to the observed values (within 5% confidence interval). For the convenience of users, Neuro-fuzzy techniques were applied to simplify the whole procedure of prediction on the clinical outcome for most clinicians with no thorough knowledge background of biostatistics. The prediction rate will improve from 80% to 98% after appropriate equation learning and training. Throughout these three different approaches, the clinical outcome prediction by algorithms for decision-making is proven effective and it really affords an evidence-based way in medical practice.

碩士
國立成功大學
環境醫學研究所
86
Occupational exposure to heat stresses has been already known to be dangerous to workers. To date, although more advanced techniques have been widely adopted by industries, however, workers might still suffer to heat exposures in some specific environments. To protect workers from ill-health effects caused by heat stresses, the government in Taiwan has promulgate the PELs in 1974, which was mainly modified from the ACGIH TLVs. However, the modification did not have any physiological basis at that time. Considering the existence of differences in race (Asian versus Caucasian) and living environment (humid and hot versus dry and cool), the feasibility of the current PELs for heat stress required further evaluations, especially on a reasonable physiological basis. In this study we have tried to build up heat exposure assessment predictive models which mainly based on the Taiwanese measured physiological data. The obtained predictive models were also used to compare with and evaluate the predictive models published by ISO on the document of ISO-7933.In this study, we found the predictive values of Cres and Eres obtained from ISO-7933 suggested models were significantly different from the theoretical values, which might mainly due to the existence of racial difference between Taiwanese and Caucasian. For this reason, two experimental predictive models for Cres and Eres were developed in this study, which were directly developed based on the measured physiological data. Due to the predictive model for skin temperature proposed by ISO was limited to specific environmental conditions, the predicted values were also found to be significantly different from the measured skin temperatures obtained from workers under wider environmental situations ( including temperature, wind velocity, and humidity, etc. ). Through multiple regression analysis, a proposed predictive model with 5 independent variables was proposed in this study for skin temperature predictions in the future. For metabolic predictions, the predictive model suggested by ISO shows that the predicted values which based on heart rate measurements did not consistent with the measured values. The relationship between heart rates and metabolic rates was further analyzed directed based on the measured values in this study, the obtained relationship not only has a higher R2, but also shows a more consistent trend. Finally, this study also shows that the predicted allowable exposure time suggested by ISO-7933 might lead to under-estimated situations as compared to the theoretical values, however, the predicted values obtained from modified predictive models do show a batter trend as used to predict allowable exposure time. It should noted here that the trend mentioned above doesn''''t have a good correlation with the experimental data, it might be due to the existence of experimental outliers because of experimental errors or intrinsic variations of tested subjects. Considering the residuals of the regression results, it is recommended in the future that the weighted-least-squares could be adopted for regression analysis to eliminate the effects caused by outliers in the future.

Personal dust measurements (n=1516) collected over the period 1981-1997 for both research and compliance purposes were used to construct two statistical models to predict historical dust exposures for a cohort of 14 B.C. sawmills and 28,000 workers. Two multiple linear regression models were built: (1) a fixed-effects model, with potential exposure determinants designated as fixed effects; and (2) a mixed-effects model, with job title designated as a random effect and all other variables as fixed effects. The two predictive models were validated against personal dust exposures (n=213) from a large interior mill that was not part of the sawmill cohort. The two models explained 36% of the dataset's variability. The predicted values were strongly correlated with observed values for both models (fixed-effects model: Pearson r=0.617; mixedeffects model: Pearson r = 0.619). The fixed-effects model predicted 56 of 58 jobs within ± 0.2 mg/m3 of the job geometric means. The mixed-effects model predicted 54 jobs within ± 0.2 mg/m . Multiple linear regressions revealed that the most important determinants of wood dust exposure were process group, coastal mill location(-), number of employees(+), and annual production levels per sawmill size(-). The two models underestimated the validation mill's geometric mean exposure level by 0.5 mg/m³ . On average, outdoor jobs were underestimated by 0.8 mg/m³ and indoor jobs by 0.3 mg/m³ . Precisions within the datasets were poor, with GSD's of bias of 2.36 for both models in the modeling dataset and 2.50 and 2.45 for the fixed-effects and mixed-effects models, respectively, in the validation dataset. The predicted values from the two models were nearly perfectly correlated for both the model building dataset (Pearson r=0.975) and the validation dataset (Pearson r=0.985). The mixed-effects model provided no improvement in predictive ability over the fixed-effects model. Several jobs in the validation mill were predicted within the range of normal day-to-day variability, but a few jobs were significantly underestimated, suggesting that the models are only generalizable to mills of similar size, level of technology, and building/yard conditions.

碩士
國立成功大學
環境醫學研究所
88
The aim of this study is set out, from the physiological thermal-hazard view of point, firstly to evaluate the feasibility of current prevention criteria for thermal environment, and then using an experimental programming method for initiating an effective engineering control strategy. A total of 197 workers, selected from four dying (including washing) plants and 6 steel casting plants, were chosen in this study to conduct both physiological thermal-hazard and environmental monitoring. In our study, we find that heat storage still can be found for those workers that have been classified into the low risk group as recommended by American Conference of Governmental Industrial Hygienists (ACGIH). The result indicates that the methodology currently recommended by ACGIH might not be adequate to protect all thermal workers. As we examine the feasibility of current WBGT limit values for workers under different work loading in various thermal environments, the results show that not only the inconsistency can be found among different plants, but also among different work loadings. Above findings suggest that the WBGT limit values that currently recommended by ACGIH might not be appropriate. For the purpose of initiating more feasible WBGT limit values, the relationship among the allowable exposure time (AET), WBGT, and metabolic rate (M) were examined on the basis of physiological view of point. By using the logistic model, the relationship can be expressed as: AETHR=60/(1＋e(7.79ln(WBGT×MHR0.30)－38.8)) (N=184, R2=0.51). Based on above relationship, we find that under light work loading (M=95.6W/m2) situation, the WBGT predicted limit values are significantly lower than current recommended values, however, under moderate (M=143.3 W/m2), heavy (M=191.1 W/m2), and extreme heavy (M=238.9 W/m2) situations, the WBGT predicted limit values are significantly higher than current recommended values. Above findings suggest that current WBGT limit values will cause the inadequate-protection of workers under light work loading situation, however, will cause over protections under the rest of situations. The last part of this study involved the use of Taguchi method to initiate an effective control strategy on the basis of evaluating the contribution of different environmental factors on the thermal strain of workers. We find that the vapor pressure (Pa) and wind speed (Va) are prioritized as the first and second important factors that will contribute to the thermal strain of workers and should be controlled when initiating an effective control strategy. The results found in this study are believed will be helpful for similar industries to enact a control strategy in the future.

碩士
國立成功大學
環境醫學研究所
104
The purpose of this study is to develop a method which combines charcoal tube organic solvent sampling results, direct-reading instrument (Photo Ionization Detector, PID) total volatile organic compounds (TVOCs) sampling results, and biological monitoring (urinary acetone) results, with a long-term database of TVOCs measured values, via the use of the Bayesian decision analysis (BDA) technique, for assessing workers’ long-term organic solvent exposures in acetone manufacturing and using industries. One acetone manufacturing industry, one quartz wafer manufacturing industry, one semiconductor component cleaning industry, and one ink production industry were selected for conducting samplings. Results show that high correlations were found (R²=0.86~0.99) between estimated acetone exposures (obtained from the area charcoal sampling results and workers’ time/activity pattern) and actual acetone exposures (using personal charcoal tube sampling results) of the designated similar exposure groups (SEGs). Workers long-term exposures estimated based BDA show that organic solvent (acetone, MEK, DMF) exposure levels for all SEGs were consistently less than 0.5 permissible exposure limit (PEL). High correlations were also found between urinary acetone exposures and workers’ personal acetone exposures (i.e., for those who wore flat activated carbon (AC) masks (R²=0.74), and AC canisters (R²=0.55), respectively). Through the use of BDA, results show all investigated SEGs’ long-term urinary acetone exposures were consistently falling between 10%~250% PEL for acetone. In conclusion, the method combined the corrected models of personal organic solvents exposures and long-term TVOCs values in the workplaces was applicable to conduct and manage long-term exposure risk for workers in acetone manufacturing and using industries.

Ozone is recognized as one of the most dangerous irritants to eyes, throat, lungs and etc .. Chamber studies consistently have demonstrated adverse effects of ozone on human lung function. The results of epidemiological studies, however, have been controversial, partly because there are many factors that affect human lung function. Thus it has been difficult to control confounding in epidemiological studies. Among these factors, retention and ventilation are two of the more important because of their strong influence on ozone's physiologically effective dose. This study used a computer simulation model, utilizing data from the "children's Camp Study", to analyze the effects of retention factors and ventilation on ozone's physiologically effective dose. The results of the simulations indicated appreciable improvement in the estimated exposure to ozone when inhaled ozone exposure (effective dose) was included in the model. These results were consistent with the study's a priori- hypothesis (that incorporating retention and ventilation factors into the model would improve the estimated exposure to ozone) primarily because of the greater precision and reduction in bias associated with the use of heart rate data that were child-and hour-specific. The study identified three simulation data sets for which the ozone dose model yielded a more significant coefficient than did the average ozone concentration model. Using the t-statistic, the three models were seen to follow the expected pattern, with statistically significant differences between the R² values (the coefficient of variation changed from 45.4 to 11.0 when the error term was 0. 01). The results of the analyses support the hypothesis that ventilation and retention factors can be used to increase the precision of ozone exposure measurement and reduce exposure assessment errors significantly, thereby sharpening the power of studies evaluating ozone's acute health effects.
Graduation date: 1992

This study investigated whether principles of the Biotic Ligand Model (BLM), originally developed to predict acute metal toxicity to fish, may be applied to metal accumulation and toxicity in short-term and long-term exposures of Hyalella azteca, an epibenthic invertebrate. In both short-term and long-term exposures, hydrogen and calcium were the only cations found to significantly influence nickel accumulation and toxicity, although some influence by magnesium was suggested in solutions of low calcium and alkalinity. Analysis of short-term (seven-day) bioaccumulation and long-term (28-day) toxicity data established three potential models to explain the observed accumulation and/or toxicity. One of the models included calcium and hydrogen as competitors to nickel (consistent with BLM theory), while the other two models incorporated the non-competitive effect of calcium on the biotic ligand in addition to, or instead of, the competitive action of calcium (not currently considered by BLM theory). Short-term accumulation observed in the tests with adults was best predicted by the competitive (BLM-type) model. However, long-term accumulation and toxicity were predicted equally well by both competitive and non-competitive models. In short-term cadmium exposures the models including calcium as a competitor, rather than an influence on the ligand, predicted accumulation and toxicity best. Hydrogen did not appear to have a significant influence. Although both competitive and non-competitive calcium models were able to predict long-term toxicity within a factor of two of the observed for most tests, the relationship between predicted and observed LC50s was not linear. Instead, for a given calcium concentration, a wide range of LC50s was observed but was associated with a relatively constant predicted LC50 (based on the influence of calcium alone). The predicted LC50s fell roughly in three lateral bands, according to calcium concentrations of approximately 0.3, 1 and 3 mmol/L. The broad range of observed LC50s associated with a relatively constant predicted LC50 reflected variability in organism response in four-week exposures in similar media and/or may have suggested that other factors influencing cadmium toxicity were not accounted for in the models.

The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.

The strong ground shaking due to the Mw7.1 Bohol Philippines earthquake left a significant imprint on its built environment. Two key factors defining this event include the wide spread of seismic intensities inferred to have shaken the island and the extensive building damage. These make the Bohol Earthquake an important opportunity to improve knowledge on building fragility and vulnerability. However, this entails a statistical description of building damage and a reliable source model for accurate estimation of earthquake ground motion. To this end, an extensive survey was conducted leading to a robust description of over 25,000 damaged and undamaged structures. This comprehensive database represents a mix of construction types at various intensity levels, in both urban and rural settings. For the ground motion estimation, the geometry and slip distribution of the finite source models were based on the analysis of SAR data, aftershocks and tele-seismic waveforms. Ground motion fields were simulated and compared using two methods including the stochastic modeling and a suite of ground motion prediction equations. The intensity-converted ground motions were calibrated and associated with the exposure-damage database to derive the empirical fragility and vulnerability models for typical building types in Bohol. These newly-derived models were used to validate the building fragility and vulnerability models already in use in the Philippines. This post-event assessment emphasizes the importance of assembling an exposure-damage database whenever damaging earthquakes occur. The sensitivity of fragility functions to ground motion inputs is also highlighted. Results indicate that the pattern of damage is best captured in the stochastic finite-fault simulation, although the Zhao et al. (2006) ground motion model registers a comparable range of ground motions. Constraints were placed on seismic building fragility and vulnerability models, which can promote more effective implementation of building regulations and construction practices as well as to deliver credible impact forecasts.

目的：本研究主要目的是建立預測模型來判定矽肺發生的累積風險從而推薦適宜的胸片照射年限並從而評估常規監測和推薦監測策略的成本效益。此外，本研究還評價了常規診斷和驗證性診斷的符合度以及在驗證性診斷中邀請的三位專家之間的符合度。
方法：總計有3492男性接塵工人在1964年1月1日到1974年12月31日期間進入本隊列並隨訪至2008年12月31日。不同閱片專家根據中國最新塵肺病診斷標準 （GBZ70-2009））分別閱片總計9084張。對專家之間閱片結果的兩兩比較和兩種診斷結果的比較均采用Cohen’s Kappa檢驗。應用三種篩選方法（強制所有變量同時進入模型，後退逐步篩選，以及Least Absolute Shrinkage and Selection Operator (LASSO)篩選。LASSO模型作為最優模型，以分數量表的方式來表達。根據分數，把工人分成高、中、低危險組，並估計這三組不同危險水平工人的累積危險度。運用0.1% 累積危險度來判定不同危险的工人及不同期別的矽肺病人的射線照射年限。多狀態Markov模型用於計算矽塵暴露工人不同狀態的年轉移概率，並應用Markov成本效益分析方法來估計每獲得一個生命年的成本效益。
結果：截至2008年底，本矽塵暴露隊列共計發現298例矽肺病人（累計發病率為8.53%），死亡1347例（死亡比例為38.57%）。本研究發現常規診斷和驗證性診斷有很好的符合度 （Kappa值為0.89， 95%可信區間為0.88-0.91）。基於LASSO模型的分數量表具有很好的診斷識別能力 （ROC曲線下面積為0.83， 95%可信區間為0.81-0.86）。根據0.1%累積危險度標準，我們判定低危險組工人第一次射線照射的時間為第11年，推薦每兩年隨訪一次；中等危險組工人和高危險組工人的第一次射線照射時間分別為第11年和第5年，推薦每年隨訪一次。矽肺病人未晉級到三期以前均一年隨訪一次。矽塵暴露工人的年轉移概率為：從健康狀態向疑似病例轉移的概率為0.0198，從疑似病例向一期矽肺轉移的概率為0.038，從一期矽肺向二期矽肺轉移的概率為0.0516，從二期矽肺向三期矽肺轉移的概率為0.059，從三期矽肺向死亡轉移的概率為0.18。在1964到2008年間，診斷一例矽肺病例平均花費醫療成本為21853.11美元，非醫療成本為5993.30美元。模擬10,000矽塵暴露工人在未來40年按照當前的狀態轉移概率，應用常規的職業健康檢查為手段獲得一個生命年的成本效益為43.60美元，應用推薦的職業健康檢查為手段獲得一個生命年的成本效益為46.99美元。
結論：本研究在最優預測模型的基礎上為不同矽肺危險度的矽塵暴露工人首次提供了科學的證據來判定射線照射的適宜年限，亦為未來矽塵暴露工人的職業健康監測提供了科學理論依據，雖然本研究推薦的監測策略獲得同常規策略相類似的成本和效益。
Objectives: The primary objective was to develop prediction models for determining the optimal intervals of chest radiographic surveillance for workers exposed to silica dust; the second primary objective is to assess the cost per case identification and compare the cost per life year gained under routine medical surveillance program with that under the recommended program for workers exposed to silica dust in China. In addition, the inter-rater agreement amongst three invited radiologists on rereading the chest radiographs and the agreement between the original diagnoses of silicosis (from routine reports) and the verified diagnoses reassessed by the three experts were also evaluated.
Methods: A total of 3492 male workers exposed to silica dust in an iron ore during the period 1964 - 1974 were recruited into this retrospective cohort study. All cohort members were followed up through the end of 2008 to observe the occurrence of silicosis and overall profile of mortality. All 9084 chest X-ray films were reread by three radiologists who had been qualified as experts at the national level according to the Chinese National Diagnostic Criteria of Pneumoconiosis (GBZ70-2009). The diagnosis of silicosis made by the panel of these three invited experts was referred to the “verified diagnosis“. Cohen’s Kappa test was used to test inter-rater agreements of three invited readers on chest radiographs and the agreement on the diagnosis of silicosis obtained from routine medical surveillance (i.e., the original routine diagnosis) was compared with those verified by the 3 qualified readers (i.e., the verified diagnosis). The multivariate Cox’s proportional hazard regression models were developed to predict the silicosis occurrence based on three selection approaches entry of all predictors at the same time, backward stepwise selection, and Least Absolute Shrinkage and Selection Operator (LASSO) selection. The LASSO model showed the best model fit which was thus regarded as the final model for predicting a score chart.
Based on this practically used score chart, we then classified workers into three groups of different risk levels of silicosis (low, moderate, and high). We estimated the cumulative risk of silicosis over years of follow-up for these three groups of workers at different risk levels. We used 1 per thousand of cumulative risk for developing silicosis as a “benchmark“ to determine the intervals of radiologic surveillance for workers with different risks of silicosis. Multi-state Markov model was used to calculate the transition probabilities of different states of silicosis and the analysis on cost and effectiveness was performed.
Results: By the end of 2008, the cumulative incidence rate of silicosis was 8.53% (298 silicosis cases) and a total of 1347 deaths (38.57%) were observed.
Good inter-rater agreements were observed amongst three invited radiologists for rereading all the chest films. Kappa value for the agreement between the original diagnoses and the verified diagnoses was 0.89 (95% confidence interval [95%CI], 0.88-0.91).
The model with the best fit was LASSO Cox model which showed a good discrimination with an area of 0.83 (95%CI, 0.81-0.86) under the receiver operating characteristic (ROC) curve. We classified workers into 3 risk groups according to the score chart obtained from the LASSO Cox model, and found the observed probabilities matched well to the predictions. According to 1 per thousand “benchmark“, we can determine that the initial interval of radiographic surveillance for workers in the low risk group was 11 years and a subsequent biyearly examination was recommended. The initial examination interval was 11 years and 5 years respectively for workers in the middle and high risk group, and then a yearly examination was recommended. For patients with silicosis, an annual radiological surveillance program was recommended regardless of the stage of pneumoconiosis.
According to results from multi-state model, we estimated that the yearly transition probability was 0.0198 for silica dust exposed workers from healthy state to the suspected silicosis cases (sojourn time = 47 years), 0.0338 from suspected silicosis cases to silicosis stage one (sojourn time = 23 years), 0.0516 from silicosis stage one to stage two (sojourn time = 9 years), 0.059 from silicosis stage two to stage three (sojourn time = 6 years), and 0.18 from silicosis stage three to death (sojourn time = 5 years).
During the period 1964 to 2008, the average direct medical cost spent on identifying one silicosis case was US$ 21853.11 and the non-medical cost for identifying one case was US$ 5993.30 per case. The estimated medical cost regarding per life year gained was US$ 43.60 under the routine medical surveillance program and it would be US$ 46.99 if the newly recommended surveillance program is adopted.
Conclusion: This study is the first to provide scientific evidence on determining the optimal intervals of radiographic surveillance for workers at different risk levels of silicosis based on the ‘best’ prediction model. Although our study revealed similar cost and effectiveness for using the recommended occupational health examination strategy compared with the routine program, this study is the first to provide scientific theory for guiding evidence-based occupational medical surveillance on workers exposed to silica dust in the world.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Chen, Minghui.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 195-210).
Abstract also in Chinese.
Abstract (English) --- p.i
Abstract (Chinese) --- p.v
Acknowledgements --- p.vii
List of contents --- p.ix
List of tables --- p.xv
List of figures --- p.xviii
List of main abbreviations --- p.xx
Chapter Section I --- Introduction and Literature Review --- p.1
Chapter Chapter 1 --- Introduction --- p.2
Chapter Chapter 2 --- Literature Review of Medical Examination, Prediction model and Economic Evaluation in Silicosis --- p.7
Chapter 2.1 --- The aims of this literature review --- p.7
Chapter 2.2 --- Search strategies and selection criteria --- p.7
Chapter 2.3 --- Searching results --- p.8
Chapter 2.4 --- Critical appraisal criteria and quality of selected studies --- p.9
Chapter 2.4.1 --- Critical appraisal criteria --- p.9
Chapter 2.4.2 --- Quality of selected studies --- p.10
Chapter 2.5 --- Overview of effectiveness of chest radiography in medical surveillance of silicosis for workers exposed to silica dust --- p.15
Chapter 2.5.1 --- Occupational medical surveillance for workers exposed to silica dust --- p.15
Chapter 2.5.2 --- Comparison of CT or HRCT and chest radiography --- p.16
Chapter 2.5.3 --- Comparison of digital radiography (DR) and chest radiography --- p.17
Chapter 2.5.4 --- Other tests to be relevant to silicosis diagnosis --- p.23
Chapter 2.5.5 --- The effectiveness of chest radiography in medical surveillance and diagnosis of silicosis --- p.24
Chapter 2.5.6 --- Comparison between the ILO Classification and the Chinese Diagnostic criteria of pneumoconiosis --- p.25
Chapter 2.6 --- Overview of application of prediction model in silicosis and a review on methodology in prediction model --- p.32
Chapter 2.6.1 --- Application of prediction model in occupational diseases --- p.32
Chapter 2.6.2 --- Overview of application of predicting model in pneumoconiosis including silicosis in China in recent 10 years --- p.34
Chapter 2.6.3 --- Development of prediction model and the applications from practical perspectives --- p.35
Chapter 2.7 --- A review on economic evaluation in occupational diseases and the screening interval analyses --- p.42
Chapter 2.7.1 --- An overview on economic evaluation in pneumoconiosis --- p.42
Chapter 2.7.2 --- Overview of economic evaluation in occupational health and safety and screening interval analyses --- p.44
Chapter 2.7.3 --- Overview for methodology of performing CEA --- p.45
Chapter 2.8 --- Research gaps were found from this literature review --- p.52
Chapter Section II --- Objectives and Methods --- p.53
Chapter Chapter 3 --- General aims and objectives --- p.54
Chapter 3.1 --- General aims --- p.54
Chapter 3.2 --- Primary objectives --- p.54
Chapter 3.3 --- Secondary objective --- p.54
Chapter Chapter 4 --- Methodology and Research Plans --- p.55
Chapter 4.1 --- Study Design --- p.55
Chapter 4.2 --- The cohort --- p.55
Chapter 4.3 --- Follow-up --- p.58
Chapter 4.4 --- Data Collection --- p.58
Chapter 4.4.1 --- Baseline information --- p.58
Chapter 4.4.2 --- Diagnosis of silicosis and the verification --- p.59
Chapter 4.4.3 --- Occupational hygiene monitoring data --- p.60
Chapter 4.4.4 --- Cost data of medical examination --- p.61
Chapter 4.5 --- Data Entry and Data Analyses --- p.62
Chapter Section III --- Results and Discussions --- p.65
Chapter Chapter 5 --- Description of the cohort --- p.66
Chapter 5.1 --- Cohort recruitment --- p.66
Chapter 5.2 --- Baseline characteristics --- p.69
Chapter 5.3 --- Change of respirable silica dust concentration over time --- p.71
Chapter 5.5 --- Occurrence of silicosis --- p.73
Chapter 5.5.1 --- Basic characteristics of silicosis patients --- p.73
Chapter 5.5.2 --- Trend of silicosis occurrence with calendar year --- p.78
Chapter 5.5.3 --- Trend of silicosis occurrence with age of entering the cohort --- p.78
Chapter 5.5.4 --- Trend of silicosis occurrence with cumulative exposure to respirable silica dust --- p.78
Chapter 5.6 --- Survival distribution at different respirable silica dust exposure levels --- p.79
Chapter 5.7 --- A summary of the results in Chapter 5 --- p.82
Chapter Chapter 6 --- Agreement between the routine diagnosis of silicosis and the verified ‘new panel’ diagnosis --- p.83
Chapter [Summary] --- p.83
Chapter 6.1 --- Background --- p.85
Chapter 6.2 --- Methodology --- p.86
Chapter 6.2.1 --- The routine and the verified diagnosis of silicosis --- p.86
Chapter 6.2.2 --- Inter-rater agreement --- p.87
Chapter 6.3 --- Results --- p.89
Chapter 6.3.1 --- Technical quality of chest X-ray films --- p.89
Chapter 6.3.2 --- Inter-rater agreement amongst readers --- p.89
Chapter 6.3.3 --- Agreement between the routine and the verified diagnosis of silicosis --- p.93
Chapter 6.3.4 --- Agreement of the progression of silicosis between the routine and verified diagnosis --- p.95
Chapter 6.4 --- Discussion --- p.97
Chapter Chapter 7 --- Developing prediction model for determining the optimal intervals of chest radiographic examinations for workers at different risks of silicosis --- p.100
Chapter [Summary] --- p.100
Chapter 7.1 --- Background --- p.102
Chapter 7.2 --- Methods --- p.104
Chapter 7.2.1 --- The cohort and outcome determination --- p.104
Chapter 7.2.2 --- Developing prediction models for silicosis --- p.107
Chapter 7.2.3 --- Coding of Predictors --- p.113
Chapter 7.3 --- Results --- p.118
Chapter 7.3.1 --- Model Specifications --- p.118
Chapter 7.3.2 --- Stepwise Selection and LASSO selection --- p.119
Chapter 7.3.3 --- Model Validations: Stability and Optimism --- p.119
Chapter 7.3.4 --- Model Presentations --- p.126
Chapter 7.3.5 --- Cut-off point of follow up year for determining examination intervals --- p.130
Chapter 7.4 --- Discussions --- p.136
Chapter Chapter 8 --- Transition probabilities of multi-states for workers with silica dust exposure --- p.141
Chapter [Summary] --- p.141
Chapter 8.1 --- Background --- p.143
Chapter 8.2 --- Methodology of multi-state model --- p.145
Chapter 8.2.1 --- Survival data and multi-state model --- p.145
Chapter 8.2.2 --- Markov model and transition states --- p.151
Chapter 8.2.3 --- Model assessment --- p.153
Chapter 8.3 --- Results --- p.154
Chapter 8.3.1 --- Initial values specification and estimates of intensity matrix --- p.154
Chapter 8.3.2 --- Transition probability matrix, mean sojourn times, and survival situation --- p.159
Chapter 8.3.3 --- Observed and expected prevalence of each state for Model assessment --- p.163
Chapter 8.4 --- Discussion --- p.165
Chapter Chapter 9 --- Cost effectiveness analysis of occupational medical surveillance for workers exposed to silica dust --- p.168
Chapter [Summary] --- p.168
Chapter 9.1 --- Background --- p.170
Chapter 9.2 --- Methodologies --- p.171
Chapter 9.2.1 --- Costs and effectiveness --- p.171
Chapter 9.2.2 --- Cost per silicosis identification estimation in the iron ore during 1964 to 2008 --- p.172
Chapter 9.2.3 --- Cost effectiveness analysis in the Markov model --- p.173
Chapter 9.3 --- Results --- p.176
Chapter 9.3.1 --- Cost estimation and cost per silicosis identification in the iron ore cohort --- p.176
Chapter 9.3.2 --- Cost effectiveness analysis in the Markov model --- p.181
Chapter 9.4 --- Discussion --- p.187
Chapter Section IV --- Conclusions and Implications --- p.191
Chapter Chapter 10 --- Conclusions, implications, and recommendations --- p.192
Chapter 10.1 --- Conclusions --- p.192
Chapter 10.2 --- Implications and recommendations --- p.193
Reference list --- p.195
Chapter Appendix I --- Chest Radiographic Imaging of Different Diagnostic Criteria for Pneumoconiosis in China --- p.211
Chapter Appendix II --- Diagnosis Stages among Different Diagnostic Criteria for Pneumoconiosis in China --- p.212
Chapter Appendix III --- Publications in journals and international conferences during the PhD study --- p.213
Chapter Supplement I --- Syntax for test proportionality of Cox model in R survival package and LASSO model in R penalized package --- p.215
Chapter Supplement II --- Guideline of applying the prediction model in practice --- p.216
Chapter Supplement III --- Syntax for multi-state model in R msm package --- p.221
Chapter Supplement IV --- An example for cost estimation of adjusting inflation and exchanging --- p.222
Chapter Supplement V --- Cost estimation of workers, suspected silicosis cases and silicosis patients in the iron ore during 1964 - 2008 --- p.223
Chapter Supplement V (Continued) --- Cost estimation of workers, suspected silicosis cases and silicosis patients in the iron ore during 1964 - 2008 --- p.224
Chapter Supplement VI --- Number of deaths for all cause of death in the iron ore cohort until 2008 --- p.225
Chapter Supplement VII --- Decision tree of Markov model in the study --- p.226
Chapter Supplement VII (Continued) --- Decision tree of Markov model in the study --- p.227