Browsing by Author "Manca, Donna"

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    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, Tyler
    Abstract 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.
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    The Alberta Pregnancy Outcomes and Nutrition (APrON) cohort study: rationale and methods
    (Maternal & Child Nutrition, 2014-01) Kaplan, Bonnie; Giesbrecht, Gerald; Leung, Brenda; Field, Catherine; Dewey, Deborah; Bell, Rhonda; Manca, Donna; O'Beirne, Maeve; Johnston, David; Pop, Victor; Singhal, Nalini; Gagnon, Lisa; Bernier, Francois; Eliasziw, Misha; McCargar, Linda; Kooistra, Libbe; Farmer, Anna; Cantell, Marja; Goonewardene, Laki; Casey, Linda; Letourneau, Nicole; Martin, Jonathan; APrON Study Team
    The Alberta Pregnancy Outcomes and Nutrition (APrON) study is an ongoing prospective cohort study that recruits pregnant women early in pregnancy and, as of 2012, is following up their infants to 3 years of age. It has currently enrolled approximately 5000 Canadians (2000 pregnant women, their offspring and many of their partners).The primary aims of the APrON study were to determine the relationships between maternal nutrient intake and status, before, during and after gestation, and (1) maternal mood; (2) birth and obstetric outcomes; and (3) infant neurodevelopment. We have collected comprehensive maternal nutrition, anthropometric, biological and mental health data at multiple points in the pregnancy and the post-partum period, as well as obstetrical, birth, health and neurodevelopmental outcomes of these pregnancies. The study continues to follow the infants through to 36 months of age.The current report describes the study design and methods, and findings of some pilot work. The APrON study is a significant resource with opportunities for collaboration.
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    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, Neil
    Abstract 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.
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    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, Tyler
    Abstract 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).