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Improving accuracy of disease prevalence estimates by combining information from administrative health records and electronic medical records

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dc.contributor.supervisor Lix, Lisa (Community Health Sciences) en_US
dc.contributor.author Al-Azazi, Saeed
dc.date.accessioned 2018-09-04T13:47:51Z
dc.date.available 2018-09-04T13:47:51Z
dc.date.issued 2018-08-13 en_US
dc.date.submitted 2018-08-13T15:23:20Z en
dc.identifier.uri http://hdl.handle.net/1993/33235
dc.description.abstract Administrative 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.rights info:eu-repo/semantics/openAccess
dc.subject Prevalence en_US
dc.subject Misclassification en_US
dc.subject Data source en_US
dc.subject Data-combining methods en_US
dc.title Improving accuracy of disease prevalence estimates by combining information from administrative health records and electronic medical records en_US
dc.type info:eu-repo/semantics/masterThesis
dc.type master thesis en_US
dc.degree.discipline Community Health Sciences en_US
dc.contributor.examiningcommittee Rabbani, Rasheda (Community Health Sciences) Singer, Alexander (Family Medicine) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2018 en_US


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