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Your search keyword '"Sanchez-Vazquez, M. J."' showing total 15 results
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15 results on '"Sanchez-Vazquez, M. J."'

5. Association between covariates and disease occurrence in the presence of diagnostic error

Author

LEWIS, F., SANCHEZ-VAZQUEZ, M. J., TORGERSON, P. R., LEWIS, F., SANCHEZ-VAZQUEZ, M. J., and TORGERSON, P. R.

Abstract

Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies

Published
2017

9. Association between covariates and disease occurrence in the presence of diagnostic error

Author

Lewis, F, Sanchez-Vazquez, M J, Torgerson, P R, Lewis, F, Sanchez-Vazquez, M J, and Torgerson, P R

Abstract

Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies.

Published
2012

14. Association between covariates and disease occurrence in the presence of diagnostic error

Author

LEWIS, F., SANCHEZ-VAZQUEZ, M. J., TORGERSON, P. R., LEWIS, F., SANCHEZ-VAZQUEZ, M. J., and TORGERSON, P. R.

Abstract

Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies

15. Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs.

Author

McCormick BJ, Sanchez-Vazquez MJ, and Lewis FI

Subjects
Animals, Bayes Theorem, Climate, Models, Statistical, Regression Analysis, Risk Factors, Seasons, Swine, United Kingdom, Weather, Swine Diseases epidemiology, Swine Diseases etiology
Abstract

Many pathogens are sensitive to climatic variables and this is reflected in their seasonality of occurrence and transmission. The identification of environmental conditions that influence disease occurrence can be subtle, particularly considering their complex interdependencies in addition to those relationships between climate and disease. Statistical treatment of environmental variables is often dependent on their correlations and thus descriptions of climate are often restricted to means rather than accounting for the more precise aspects (including mean, maximum, minimum, variability). Here we utilize a novel multivariate statistical modelling approach, additive Bayesian network (ABN) analyses, to identify the inter-linkages of different weather variables to better capture short-term environmental conditions that are important drivers of disease. We present a case study that explores weather as a driver of disease in livestock systems. We utilize quality assurance health scheme data on ten major diseases of pigs from 875 finishing pig herds distributed across the United Kingdom over 7 years (2005-2011). We examine the relationship between the occurrence of these pathologies and contemporary weather conditions measured by local meteorological stations. All ten pathologies were associated with at least 2 other pathologies (maximum 6). Three pathologies were associated directly with temperature variables: papular dermatitis, enzootic pneumonia and milk spots. Latitude was strongly associated with multiple pathologies, though associations with longitude were eliminated when clustering for repeated observations of farms was assessed. The identification of relationships between climatic factors and different (potentially related) diseases offers a more comprehensive insight into the complex role of seasonal drivers and herd health status than traditional analytical methods., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Published
2013
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