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N/A / Thesis / Master of Science (MS)

Biological and therapeutic agents are used in food animal production to maintain animal health and well being, prevent and treat disease, and to maintain or enhance production. Concerns about the use of pharmaceutical agents in food animal production have been raised, especially in relation to food quality and safety. This study addressed the scarcity of information concerning the quantity of pharmaceuticals being used and the reasons for their use in sheep production. Additional goals included determining the sources of information used by shepherds in making treatment decisions and evaluating the economic impact that pharmaceutical usage has on sheep production. Thirty-nine Virginia sheep producers participated in this study of four months duration from March through September. After completing an initial questionnaire to determine flock and management characteristics, participants were asked to record all treatments with biological and therapeutic agents that occurred within their sheep flocks. A total of 14,310 treatments were recorded for a median of 1.5 treatments per sheep per month. Parasite control and vaccination were the most frequent reasons for treatment (64.9% and 15.2%, respectively) with vitamin/mineral supplementation being the next most common (8.8%). Price information was collected for 13,912 treatment events. An estimated total of $7,523.78 was spent on pharmaceutical treatment over the course of the study. This amounts to a cost of $0.63 per sheep per month of observation. Results of this study will enhance the ability of producers to evaluate treatment decisions, allow comparisons to be made between operations and provide a base of information for future research. / Master of Science

Yes / We present a thorough review of hyperheuristic research to date, and analyse/compare hyperheuristic papers based on the methods used.

No / Is midwifery knowledge a relevant construct for contemporary practice? In other words, is it useful to think about the things midwives know and the skills they possess in terms of midwifery knowledge? Or, in an era of interprofessional practice, clinical governance and risk management, have midwives lost whatever was unique to their profession about how to help women give birth?

Brugha and his colleagues in this issue raise important questions about the validity of standardized diagnostic interviews of mental disorders, such as the Composite International Diagnostic Interview (CIDI) (WHO, 1990). Although their concerns refer predominantly to the use of such instruments in epidemiological research, the authors' conclusions also have significant implications for diagnostic assessments in clinical practice and research. We agree with Brugha et al. that the inflexible approach to questioning used in standardized interviews can lead to an increased risk of invalidity with regard to some diagnoses. We also agree that the use of more semi-structured clinical questions has the potential to address this problem. However, we disagree with Brugha et al. in several other respects.

Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.

Data obtained from longitudinal surveys using complex multi-stage sampling designs contain cross-sectional dependencies among units caused by inherent hierarchies in the data, and within subject correlation arising due to repeated measurements. The statistical methods used for analyzing such data should account for stratification, clustering and unequal probability of selection as well as within-subject correlations due to repeated measurements. <p>The complex multi-stage design approach has been used in the longitudinal National Population Health Survey (NPHS). This on-going survey collects information on health determinants and outcomes in a sample of the general Canadian population. <p>This dissertation compares the model-based and design-based approaches used to determine the risk factors of asthma prevalence in the Canadian female population of the NPHS (marginal model). Weighted, unweighted and robust statistical methods were used to examine the risk factors of the incidence of asthma (event history analysis) and of recurrent asthma episodes (recurrent survival analysis). Missing data analysis was used to study the bias associated with incomplete data. To determine the risk factors of asthma prevalence, the Generalized Estimating Equations (GEE) approach was used for marginal modeling (model-based approach) followed by Taylor Linearization and bootstrap estimation of standard errors (design-based approach). The incidence of asthma (event history analysis) was estimated using weighted, unweighted and robust methods. Recurrent event history analysis was conducted using Anderson and Gill, Wei, Lin and Weissfeld (WLW) and Prentice, Williams and Peterson (PWP) approaches. To assess the presence of bias associated with missing data, the weighted GEE and pattern-mixture models were used.<p>The prevalence of asthma in the Canadian female population was 6.9% (6.1-7.7) at the end of Cycle 5. When comparing model-based and design- based approaches for asthma prevalence, design-based method provided unbiased estimates of standard errors. The overall incidence of asthma in this population, excluding those with asthma at baseline, was 10.5/1000/year (9.2-12.1). For the event history analysis, the robust method provided the most stable estimates and standard errors. <p>For recurrent event history, the WLW method provided stable standard error estimates. Finally, for the missing data approach, the pattern-mixture model produced the most stable standard errors <p>To conclude, design-based approaches should be preferred over model-based approaches for analyzing complex survey data, as the former provides the most unbiased parameter estimates and standard errors.