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dc.contributor.supervisorLix, Lisa (Community Health Sciences)en_US
dc.contributor.authorAl-Azazi, Saeed
dc.date.accessioned2018-09-04T13:47:51Z
dc.date.available2018-09-04T13:47:51Z
dc.date.issued2018-08-13en_US
dc.date.submitted2018-08-13T15:23:20Zen
dc.identifier.urihttp://hdl.handle.net/1993/33235
dc.description.abstractAdministrative health records (AHRs) and electronic medical records (EMRs) are the two main sources of population-based data for chronic disease surveillance in Canada. Misclassification errors exist in both databases, which can bias estimates of disease prevalence and incidence. The objectives were to evaluate the accuracy of rule-based and probabilistic-based methods to combine error-prone sources using computer simulation and to demonstrate how to use these methods with a numeric example. Four data-combining methods were compared: rule-based ‘OR’ method, rule-based ‘AND’ method, rule-based sensitivity-specificity adjusted (RSSA) method and probabilistic-based sensitivity-specificity adjusted (PSSA) method. The methods were demonstrated using linked AHRs and EMRs to ascertain cases of hypertension. The ‘OR’ and ‘AND’ methods are recommended when there is sufficient overlap between measures of disease status. The RSSA method depends on the choice of sensitivity and specificity estimates. The PSSA method performs well when true prevalence is high and correlations amongst covariates are low.en_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPrevalenceen_US
dc.subjectMisclassificationen_US
dc.subjectData sourceen_US
dc.subjectData-combining methodsen_US
dc.titleImproving accuracy of disease prevalence estimates by combining information from administrative health records and electronic medical recordsen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineCommunity Health Sciencesen_US
dc.contributor.examiningcommitteeRabbani, Rasheda (Community Health Sciences) Singer, Alexander (Family Medicine)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteOctober 2018en_US


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