Browsing by Author "Garies, Stephanie"
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Item Open Access A cross-sectional study evaluating cardiovascular risk and statin prescribing in the Canadian Primary Care Sentinel Surveillance Network database(2022-05-25) Johnston, Ian S.; Miles, Brendan; Soos, Boglarka; Garies, Stephanie; Perez, Grace; Queenan, John A.; Drummond, Neil; Singer, AlexanderAbstract Background Cardiovascular disease (CVD) is a major cause of morbidity and mortality in Canada. Assessment and management of CVD risk is essential in reducing disease burden. This includes both clinical risk factors and socioeconomic factors, though few studies report on socioeconomic status in relation to CVD risk and treatment. The primary objective of this study was to estimate the cardiovascular risk of patients attending primary care practices across Canada; secondly, to evaluate concordance with care indicators suggested by current clinical practice guidelines for statin prescribing according to patients’ cardiovascular risk and socioeconomic status. Methods This cross-sectional observational study used the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database, which is comprised of clinical data from primary care electronic medical records. Patients aged 35-75y with at least one visit to their primary care provider between 2012 and 2016 were included. Patients were assigned to a CVD risk category (high, medium, low) and a deprivation quintile was calculated for those with full postal code available. Descriptive analyses were used to determine the proportion of patients in each risk category. Logistic regression was used to evaluate the consistency of statin prescribing according to national clinical guidelines by risk category and deprivation quintile. Results A total of 324,526 patients were included. Of those, 116,947 (36%) of patients were assigned to a high CVD risk category, primarily older adults, males, and those with co-morbidities. There were statistically significant differences between least (quintile 1) and most (quintile 5) deprived socioeconomic quintiles, with those at high CVD risk disproportionately in Q5 (odds ratio 1.4). Overall, 48% of high-risk patients had at least one statin prescription in their record. Patients in the lower socioeconomic groups had a higher risk of statin treatment which deviated from clinical guidelines. Conclusions Primary care patients who are at high CVD risk are more often male, older, have more co-morbidities and be assigned to more deprived SES quintiles, compared to those at low CVD risk. Additionally, patients who experience more challenging socioeconomic situations may be less likely to receive CVD treatment that is consistent with care guidelines.Item Open Access A data quality assessment to inform hypertension surveillance using primary care electronic medical record data from Alberta, Canada(2021-02-02) Garies, Stephanie; McBrien, Kerry; Quan, Hude; Manca, Donna; Drummond, Neil; Williamson, TylerAbstract Background Hypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance. Methods We conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance. Results Patient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available. Conclusions Blood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.Item Open Access Assessing the suitability of general practice electronic health records for clinical prediction model development: a data quality assessment(2021-10-30) Thuraisingam, Sharmala; Chondros, Patty; Dowsey, Michelle M.; Spelman, Tim; Garies, Stephanie; Choong, Peter F.; Gunn, Jane; Manski-Nankervis, Jo-AnneAbstract Background The use of general practice electronic health records (EHRs) for research purposes is in its infancy in Australia. Given these data were collected for clinical purposes, questions remain around data quality and whether these data are suitable for use in prediction model development. In this study we assess the quality of data recorded in 201,462 patient EHRs from 483 Australian general practices to determine its usefulness in the development of a clinical prediction model for total knee replacement (TKR) surgery in patients with osteoarthritis (OA). Methods Variables to be used in model development were assessed for completeness and plausibility. Accuracy for the outcome and competing risk were assessed through record level linkage with two gold standard national registries, Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) and National Death Index (NDI). The validity of the EHR data was tested using participant characteristics from the 2014–15 Australian National Health Survey (NHS). Results There were substantial missing data for body mass index and weight gain between early adulthood and middle age. TKR and death were recorded with good accuracy, however, year of TKR, year of death and side of TKR were poorly recorded. Patient characteristics recorded in the EHR were comparable to participant characteristics from the NHS, except for OA medication and metastatic solid tumour. Conclusions In this study, data relating to the outcome, competing risk and two predictors were unfit for prediction model development. This study highlights the need for more accurate and complete recording of patient data within EHRs if these data are to be used to develop clinical prediction models. Data linkage with other gold standard data sets/registries may in the meantime help overcome some of the current data quality challenges in general practice EHRs when developing prediction models.Item Open Access Correction: Development and validation of a case definition for problematic menopause in primary care electronic medical records(2023-10-19) Pham, Anh N.; Cummings, Michael; Yuksel, Nese; Sydora, Beate; Williamson, Tyler; Garies, Stephanie; Pilling, Russell; Lindeman, Cliff; Ross, SueItem Open Access Development and validation of a case definition for problematic menopause in primary care electronic medical records(2023-10-05) Pham, Anh N.; Cummings, Michael; Yuksel, Nese; Sydora, Beate; Williamson, Tyler; Garies, Stephanie; Pilling, Russell; Lindeman, Cliff; Ross, SueAbstract Background Menopause is a normal transition in a woman’s life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. This study developed and validated a case definition for problematic menopause using Canadian primary care electronic medical records, which is an essential step in examining the condition and improving quality of care. Methods We used data from the Canadian Primary Care Sentinel Surveillance Network including billing and diagnostic codes, diagnostic free-text, problem list entries, medications, and referrals. These data formed the basis of an expert-reviewed reference standard data set and contained the features that were used to train a machine learning model based on classification and regression trees. An ad hoc feature importance measure coupled with recursive feature elimination and clustering were applied to reduce our initial 86,000 element feature set to a few tens of the most relevant features in the data, while class balancing was accomplished with random under- and over-sampling. The final case definition was generated from the tree-based machine learning model output combined with a feature importance algorithm. Two independent samples were used: one for training / testing the machine learning algorithm and the other for case definition validation. Results We randomly selected 2,776 women aged 45–60 for this analysis and created a case definition, consisting of two occurrences within 24 months of International Classification of Diseases, Ninth Revision, Clinical Modification code 627 (or any sub-codes) OR one occurrence of Anatomical Therapeutic Chemical classification code G03CA (or any sub-codes) within the patient chart, that was highly effective at detecting problematic menopause cases. This definition produced a sensitivity of 81.5% (95% CI: 76.3-85.9%), specificity of 93.5% (91.9-94.8%), positive predictive value of 73.8% (68.3-78.6%), and negative predictive value of 95.7% (94.4-96.8%). Conclusion Our case definition for problematic menopause demonstrated high validity metrics and so is expected to be useful for epidemiological study and surveillance. This case definition will enable future studies exploring the management of menopause in primary care settings.Item Open Access Documenting cannabis use in primary care: a descriptive cross-sectional study using electronic medical record data in Alberta, Canada(2023-02-01) Soos, Boglarka; Garies, Stephanie; Cornect-Benoit, Ashley; Montgomery, Lori; Sharpe, Heather; Rittenbach, Katherine; Manca, Donna; Duerksen, Kimberley; Forst, Brian; Drummond, NeilAbstract Objective Documenting cannabis use is important for patient care, but no formal requirements for consistent reporting exist in primary care. The objective of this study was to understand how cannabis use is documented in primary care electronic medical record (EMR) data. Results This was a cross-sectional study using de-identified EMR data from over 398,000 patients and 333 primary care providers in Alberta, Canada. An automated pattern-matching algorithm was developed to identify text and ICD-9 diagnostic codes indicating cannabis use in the EMR. There was a total of 11,724 records indicating cannabis use from 4652 patients, representing approximately 1.2% of the patient sample. Commonly used terms and ICD-9 codes included cannabis, marijuana/marihuana, THC, 304.3 and 305.2. Nabilone was the most frequently prescribed cannabinoid medication. Slightly more males and those with a chronic condition had cannabis use recorded more often. Overall, very few patients have cannabis use recorded in primary care EMR data and this is not captured in a systematic way. We propose several strategies to improve the documentation of cannabis use to facilitate more effective clinical care, research, and surveillance.Item Open Access Enhancing Primary Care Electronic Medical Record (EMR) Data in Alberta by Quality Assessment, Data Processing, and Linkage to Administrative Data(2020-07-01) Garies, Stephanie; Quan, Hude; Williamson, Tyler S.; Drummond, Neil A.; McBrien, Kerry AlisonThe growth of electronic medical record (EMR) systems in healthcare settings has created opportunities for EMR data to be reused for secondary purposes. Since EMR data are generated from clinical and administrative processes, the suitability for other uses (e.g. surveillance or research) is questionable. Assessing data quality is important for understanding the database contents, identifying potential limitations or biases, and determining how ‘fit for purpose’ the data are. This thesis focused on evaluating and improving the quality of primary care EMR data in Alberta. Data quality, which is highly contextual, was examined from the perspective of use for hypertension surveillance, as hypertension is a prevalent chronic condition associated with poor health outcomes and high cost implications. The first part of this thesis involved developing a comprehensive description of EMR data capture, extraction, and processing by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Alberta. The second section presented a data quality assessment using CPCSSN data elements relevant to hypertension surveillance. The third part explored multiple imputation and a pattern-matching algorithm for improving smoking status records in the EMR data. Lastly, EMR and administrative data for a cohort of hypertensive patients were linked and described. The CPCSSN process documentation and data quality assessment created novel, useful, and comprehensive information for data users. CPCSSN data appear to be suitable for hypertension surveillance, though caution is warranted for several variables of inconsistent quality. Multiple imputation improved completeness of patient smoking statuses, but the lack of an appropriate external reference source made confirming accuracy difficult. The pattern-matching algorithm demonstrated high accuracy for categorizing smoking status; however, it missed classifying 24% of patients. Lastly, EMR data for 6,307 hypertensive patients were successfully linked to five administrative databases. Although this linked sample is relatively small and may be subject to selection bias (limiting the generalizability for surveillance purposes), the cohort could be useful for health outcomes research or validating elements in the EMR or administrative databases. This work has informed the development of more efficient processes for EMR-administrative linkages. Data quality assessment outcomes will be made available to inform various types of CPCSSN data users.Item Open Access Identification of validated case definitions for chronic disease using electronic medical records: a systematic review protocol(2017-02-23) Souri, Sepideh; Symonds, Nicola E; Rouhi, Azin; Lethebe, Brendan C; Garies, Stephanie; Ronksley, Paul E; Williamson, Tyler S; Fabreau, Gabriel E; Birtwhistle, Richard; Quan, Hude; McBrien, Kerry AAbstract Background Primary care electronic medical record (EMR) data are being used for research, surveillance, and clinical monitoring. To broaden the reach and usability of EMR data, case definitions must be specified to identify and characterize important chronic conditions. The purpose of this study is to identify all case definitions for a set of chronic conditions that have been tested and validated in primary care EMR and EMR-linked data. This work will provide a reference list of case definitions, together with their performance metrics, and will identify gaps where new case definitions are needed. Methods We will consider a set of 40 chronic conditions, previously identified as potentially important for surveillance in a review of multimorbidity measures. We will perform a systematic search of the published literature to identify studies that describe case definitions for clinical conditions in EMR data and report the performance of these definitions. We will stratify our search by studies that use EMR data alone and those that use EMR-linked data. We will compare the performance of different definitions for the same conditions and explore the influence of data source, jurisdiction, and patient population. Discussion EMR data from primary care providers can be compiled and used for benefit by the healthcare system. Not only does this work have the potential to further develop disease surveillance and health knowledge, EMR surveillance systems can provide rapid feedback to participating physicians regarding their patients. Existing case definitions will serve as a starting point for the development and validation of new case definitions and will enable better surveillance, research, and practice feedback based on detailed clinical EMR data. Systematic review registration PROSPERO CRD42016040020Item Open Access Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms(2020-03-14) Garies, Stephanie; Cummings, Michael; Quan, Hude; McBrien, Kerry; Drummond, Neil; Manca, Donna; Williamson, TylerAbstract Background Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients’ health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database. Methods EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016–2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey. Results Among those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category. Conclusion Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).