No description available.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

M. Tech. / Background: Groundwater is essential for many human activities, especially as a source of drinking water. Contaminated groundwater threatens many valuable water resources which may have adverse human health consequences. Groundwater contamination often goes unnoticed as it occurs underground and it is often impossible to, and often very expensive to rehabilitate. Groundwater pollution originating from industrial sites is a general problem in many industrialized countries, but also in developing countries such as South Africa. Among the various types of groundwater contamination, DNAPL (Dense Non-Aqueous Phase Liquids) contamination is a common one. A generic test for determining DNAPL contamination in water and their adverse effects on human health that consume this water does not exist. This study was aimed, by using two known immunoassays, to asses the toxicity and DNA damaging potential, using known techniques, of two index DNAPL and untested groundwater from newly drilled boreholes in areas suspected of DNAPL contamination. Objectives: To compile a DNAPL guideline based the use of index chemicals (Tricloroethylene [TCE] and Aroclor® 1254 [ARO]) to determine the high-end values and a series of reference (non-contaminated ground, and other treated waters) samples used to determine low-end values. PBMC were exposed to these samples and the test reactions for cytotoxicity and DNA damage measured. The methodology was then applied to groundwaters taken from an industrial site. This was to establish the health effects of these waters as well as determining whether they had DNAPL contamination.

No description available.

published_or_final_version / Zoology / Doctoral / Doctor of Philosophy

Background and Purpose: Quality measurement is an essential, yet, complex component of mental health services that is often limited by a lack of clinically meaningful data across service providers. Understanding how services are organized, delivered, and effective is vital for ensuring and improving health care quality. In quality measurement of mental healthcare, structural indicators are common with fewer process and outcome indicators available. Using data from the RAI - Mental Health (RAI-MH), a comprehensive assessment system mandated for use in Ontario, this dissertation aims to define a set of mental health quality indicators (MHQIs), effectiveness quality indicators (EQIs), and risk adjustment strategy that can be used to evaluate and compare quality at the facility- and regional-levels. Methodology: The MHQIs were developed using a retrospective analysis of two data sets: A pilot sample of 1,056 RAI-MH admission and discharge assessments collected from 7 inpatient mental health units in Ontario and a sample of 30,046 RAI-MH admission and discharge assessments collected from 70 Ontario hospitals as part of the Canadian Institute for Health Information Ontario Mental Health Reporting System. The MHQIs were chosen based on clinically meaningful domains identified by mental health and quality stakeholders, MHQI rates that were consistently above 5% or below 95% among hospitals, and appropriate variation in rates among hospitals in both sets of data. For each MHQI domain, regression modeling using generalized estimating equations was employed to choose risk adjustment variables and logistic or linear regression was used to perform risk adjustment to compare MHQI and EQI rates among hospitals and regions. Results: A set of 27 MHQIs was defined measuring improvement and incidence/failure to improve in the following domains: depressive/psychosis/pain symptoms, cognitive/physical/social functioning, aggressive/ disruptive/violent behaviours, and control procedures. Also, 13 EQIs were defined to identify the magnitude of change in MHQI domains per 7 days between assessments. Regression models using generalized estimating equations identified between 1 and 8 risk adjustment covariates for each MHQI. Risk adjustment using logistic and linear regression resulted in over 50% of hospitals and LHINs changing in rank based on MHQI and EQI scores. Conclusion: This dissertation has developed an evidence-based set of MHQIs and EQIs based on a clinically rich set of data. Since the data is available provincially, the MHQIs and EQIs can be used for hospital based, regional, and public reports on quality of inpatient mental health services. The MHQIs/EQIs can be linked to care planning and funding using the RAI-MH to promote quality improvement and accountability for recipients, providers, managers, governors, and funders of mental health services. Opportunities are also available to extend the use of the MHQIs to community mental health, so that system level evaluations of quality can be developed.

Background and Purpose: Quality measurement is an essential, yet, complex component of mental health services that is often limited by a lack of clinically meaningful data across service providers. Understanding how services are organized, delivered, and effective is vital for ensuring and improving health care quality. In quality measurement of mental healthcare, structural indicators are common with fewer process and outcome indicators available. Using data from the RAI - Mental Health (RAI-MH), a comprehensive assessment system mandated for use in Ontario, this dissertation aims to define a set of mental health quality indicators (MHQIs), effectiveness quality indicators (EQIs), and risk adjustment strategy that can be used to evaluate and compare quality at the facility- and regional-levels. Methodology: The MHQIs were developed using a retrospective analysis of two data sets: A pilot sample of 1,056 RAI-MH admission and discharge assessments collected from 7 inpatient mental health units in Ontario and a sample of 30,046 RAI-MH admission and discharge assessments collected from 70 Ontario hospitals as part of the Canadian Institute for Health Information Ontario Mental Health Reporting System. The MHQIs were chosen based on clinically meaningful domains identified by mental health and quality stakeholders, MHQI rates that were consistently above 5% or below 95% among hospitals, and appropriate variation in rates among hospitals in both sets of data. For each MHQI domain, regression modeling using generalized estimating equations was employed to choose risk adjustment variables and logistic or linear regression was used to perform risk adjustment to compare MHQI and EQI rates among hospitals and regions. Results: A set of 27 MHQIs was defined measuring improvement and incidence/failure to improve in the following domains: depressive/psychosis/pain symptoms, cognitive/physical/social functioning, aggressive/ disruptive/violent behaviours, and control procedures. Also, 13 EQIs were defined to identify the magnitude of change in MHQI domains per 7 days between assessments. Regression models using generalized estimating equations identified between 1 and 8 risk adjustment covariates for each MHQI. Risk adjustment using logistic and linear regression resulted in over 50% of hospitals and LHINs changing in rank based on MHQI and EQI scores. Conclusion: This dissertation has developed an evidence-based set of MHQIs and EQIs based on a clinically rich set of data. Since the data is available provincially, the MHQIs and EQIs can be used for hospital based, regional, and public reports on quality of inpatient mental health services. The MHQIs/EQIs can be linked to care planning and funding using the RAI-MH to promote quality improvement and accountability for recipients, providers, managers, governors, and funders of mental health services. Opportunities are also available to extend the use of the MHQIs to community mental health, so that system level evaluations of quality can be developed.

published_or_final_version / Chemistry / Master / Master of Philosophy

published_or_final_version / Architecture / Master / Master of Philosophy

A statistical analysis of water quality data collected on paired agricultural watersheds was undertaken. The objective of the study was to evaluate trends in water quality. The data sets that were used to determine the changes in water quality were taken from the project "Gestion de leau dans le bassin versant de la partie superieure du ruisseau St. Esprit". For the period from 1994 to 1996, the analysed water quality parameter were nitrate, phosphate, ammonium, potassium, total Kjeldahl nitrogen, total phosphorus and suspended sediment. / The data sets were analysed using descriptive statistics, graphical techniques and non-parametric methods to detect trends in the measured water quality parameters. The statistical analyses were undertaken to determine the effects of soil conservation practices and fertiliser management and to compare different sampling strategies. / The analyses showed that there were no detectable changes in water quality over the 3-year period related to the conservation practices. The lack of improvement in water quality might be due to the slow rate of adoption of conservation practices and to climatic variations. / For the non-parametric methods applied, it was possible to show that climatic variations on small watershed affect the results over a short time period (<5 years). The phosphate concentration on the control showed a significant upward trend. The nitrate concentration on St. Esprit showed an upward trend over the 3-year period and then downward trend after a 4-year period of water quality data. This was likely due to the implementation of best management practices. / The statistical analyses showed that weekly sampling on fixed schedule produce the same results as automated sampling based upon flow rate related to a defined discharge. This shows that the more complex and expensive flow weighted sampling scheme is not required to detect trends in water quality.