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BackgroundElectronic Medical Records (EMRs) are increasingly used in the provision of primary care and have been compiled into databases which can be utilized for surveillance, research and informing practice. The primary purpose of these records is for the provision of individual patient care; validation and examination of underlying limitations is crucial for use for research and data quality improvement. This study examines and describes the validity of chronic disease case definition algorithms and factors affecting data quality in a primary care EMR database.MethodsA retrospective chart audit of an age stratified random sample was used to validate and examine diagnostic algorithms applied to EMR data from the Manitoba Primary Care Research Network (MaPCReN), part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The presence of diabetes, hypertension, depression, osteoarthritis and chronic obstructive pulmonary disease (COPD) was determined by review of the medical record and compared to algorithm identified cases to identify discrepancies and describe the underlying contributing factors.ResultsThe algorithm for diabetes had high sensitivity, specificity and positive predictive value (PPV) with all scores being over 90%. Specificities of the algorithms were greater than 90% for all conditions except for hypertension at 79.2%. The largest deficits in algorithm performance included poor PPV for COPD at 36.7% and limited sensitivity for COPD, depression and osteoarthritis at 72.0%, 73.3% and 63.2% respectively. Main sources of discrepancy included missing coding, alternative coding, inappropriate diagnosis detection based on medications used for alternate indications, inappropriate exclusion due to comorbidity and loss of data.ConclusionsComparison to medical chart review shows that at MaPCReN the CPCSSN case finding algorithms are valid with a few limitations. This study provides the basis for the validated data to be utilized for research and informs users of its limitations. Analysis of underlying discrepancies provides the ability to improve algorithm performance and facilitate improved data quality.

IntroductionTobacco dependence and smoke exposure have been global epidemics with health consequences recognised by the US Surgeon General since the 1960s and 1970s, respectively. During this period, a vast body of research evidence has emerged including many reviews of primary research studies targeting various tobacco control strategies. Published review studies synthesise primary evidence, providing a rich source for mapping the broad range of topics and research foci along with revealing areas of evidence deficits. In this paper, we outline our scoping review protocol to systematically review published review articles specific to tobacco control and primary prevention over the last 10 years.Methods and analysisUsing Arksey and O'Malley's scoping review methodology as a guide, our scoping review of published reviews begins by searching several databases: PubMed, Scopus, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo and the Educational Resources Information Centre (ERIC). Our multidisciplinary team has formulated search strategies and two reviewers will independently screen eligible studies for final study selection. Bibliographic data and abstract content will be collected and analysed using a tool developed iteratively by the research team.Ethics and disseminationA scoping review of published review articles is a novel approach for examining the breadth of literature regarding tobacco control strategies and, as a secondary analysis, does not require ethics approval. We anticipate results will identify research gaps as well as novel ideas for primary prevention research specific to tobacco control strategies concerning intervention, programming and policy. Although this is our first step in establishing a foundation for a research agenda, we will be disseminating results through journals and conferences targeting primary care providers and tobacco control.

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