PRISM :: Browsing by Author "Williamson, Tyler"

Browsing by Author "Williamson, Tyler"

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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, 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.
Open Access
Advancing Concussion Assessment in Pediatrics (A-CAP): a prospective, concurrent cohort, longitudinal study of mild traumatic brain injury in children: protocol study
(BMJ, 2017-07-01) Yeates, Keith O.; Beauchamp, Miriam; Craig, William; Doan, Quynh; Zemek, Roger; Bjornson, Bruce H.; Gravel, Jocelyn; Mikrogianakis, Angelo; Goodyear, Bradley; Abdeen, Nishard; Beaulieu, Christian; Dehaes, Mathieu; Deschenes, Sylvain; Harris, Ashley D.; Lebel, Catherine; Lamont, Ryan; Williamson, Tyler; Barlow, Karen M.; Bernier, Francois; Brooks, Brian L.; Emery, Carolyn; Freedman, Stephen B.; Kowalski, Kristina; Mrklas, Kelly; Tomfohr-Madsen, Lianne; Schneider, Kathryn J.
Introduction Paediatric mild traumatic brain injury (mTBI) is a public health burden. Clinicians urgently need evidence-based guidance to manage mTBI, but gold standards for diagnosing and predicting the outcomes of mTBI are lacking. The objective of the Advancing Concussion Assessment in Pediatrics (A-CAP) study is to assess a broad pool of neurobiological and psychosocial markers to examine associations with postinjury outcomes in a large sample of children with either mTBI or orthopaedic injury (OI), with the goal of improving the diagnosis and prognostication of outcomes of paediatric mTBI. Methods and analysis A-CAP is a prospective, longitudinal cohort study of children aged 8.00-16.99 years with either mTBI or OI, recruited during acute emergency department (ED) visits at five sites from the Pediatric Emergency Research Canada network. Injury information is collected in the ED; follow-up assessments at 10 days and 3 and 6 months postinjury measure a variety of neurobiological and psychosocial markers, covariates/confounders and outcomes. Weekly postconcussive symptom ratings are obtained electronically. Recruitment began in September 2016 and will occur for approximately 24 months. Analyses will test the major hypotheses that neurobiological and psychosocial markers can: (1) differentiate mTBI from OI and (2) predict outcomes of mTBI. Models initially will focus within domains (eg, genes, imaging biomarkers, psychosocial markers), followed by multivariable modelling across domains. The planned sample size (700 mTBI, 300 OI) provides adequate statistical power and allows for internal cross-validation of some analyses. Ethics and dissemination The ethics boards at all participating institutions have approved the study and all participants and their parents will provide informed consent or assent. Dissemination will follow an integrated knowledge translation plan, with study findings presented at scientific conferences and in multiple manuscripts in peer-reviewed journals.
Open Access
Analgesic Use Among Adults with a Trauma-Related Emergency Department Visit: A Retrospective Cohort Study from Alberta, Canada
(2023-06-03) Sevcik, Bill; Lobay, Kevin; Luu, Huong; Martins, Karen J. B.; Vu, Khanh; Nguyen, Phuong U.; Bohlouli, Solmaz; Eurich, Dean T.; Lester, Erica L. W.; Williamson, Tyler; Richer, Lawrence; Klarenbach, Scott W.
Abstract Introduction A better understanding of current acute pain-driven analgesic practices within the emergency department (ED) and upon discharge will provide foundational information in this area, as few studies have been conducted in Canada. Methods Administrative data were used to identify adults with a trauma-related ED visit in the Edmonton area in 2017/2018. Characteristics of the ED visit included time from initial contact to analgesic administration, type of analgesics dispensed during and upon being discharged home directly from the ED (≤ 7 days after), and patient characteristics. Results A total of 50,950 ED visits by 40,505 adults with trauma were included. Analgesics were administered in 24.2% of visits, of which non-opioids were dispensed in 77.0% and opioids were dispensed in 49.0%. Time to analgesic initiation occurred more than 2 h after first contact. Upon discharge, 11.5% received a non-opioid and 15.2% received an opioid analgesic, among whom 18.5% received a daily dose ≥ 50 morphine milligram equivalents (MME) and 30.2% received > 7 days of supply. Three hundred and seventeen adults newly met criteria for chronic opioid use after the ED visit, among whom 43.5% received an opioid dispensation upon discharge; of these individuals, 26.8% had a daily dose ≥ 50 MME and 65.9% received > 7 days of supply. Conclusions Findings can be used to inform optimization of analgesic pharmacotherapy practices for the treatment of acute pain, which may include reducing the time to initiation of analgesics in the ED, as well as close consideration of recommendations for acute pain management upon discharge to provide ideal patient-centered, evidence-informed care.
Open Access
Big data and machine learning tools to understand mastitis epidemiology and other topics
(2021-11) Naqvi, Syed Ali; Barkema, Herman W.; Deardon, Rob; Williamson, Tyler; Dufour, Simon
Increased availability of technologies to collect and store individual health data is leading to a growing interest in applying Big Data analytical methodologies to better understand health and disease in both humans and dairy cattle. Data collected through routine observations such as doctor or veterinary visits, milking equipment, or remote sensors can be successfully incorporated to monitor and manage individual and public health, and support operational decision-making on dairy farms. These sources of data also provide an invaluable resource in conducting epidemiological and health research, provided they are appropriately handled during the statistical analysis. In this thesis, 1) data from bacteriological sampling were combined with regularly collected dairy herd improvement (DHI) data to describe udder health in primiparous dairy cattle across Canada; 2) a systematic review and meta-analysis was conducted to synthesize all available research on the effectiveness of pre-calving therapies to improve udder health in primiparous dairy cattle; 3) a model was developed for the detection of clinical mastitis (CM) based on routinely collected data from automated milking systems (AMS); 4) a simulation study assessed the impact of unmeasured heterogeneity in secondary data collected from multiple dairy farms on the performance of a model trained to detect CM onset; 5) the immune fingerprint of children presenting with symptoms of appendicitis are compared by combining emergency department admissions data with results from a multiplex cytokine assay and 6) dietary risk factors for immunological flare-ups in patients with Crohn’s disease are explored by combining patient-reported dietary records with results of a multiplex cytokine assay. Chapter 2 demonstrated that the udder health in Canadian primiparous dairy cows was an issue that needed attention, and chapter 3 demonstrated that pre-calving treatments of different types can be effective at improving udder health in early lactation. Both chapters highlighted the need for routinely collected data to be combined with targeted data collection (monitoring of non-milking dairy cows, culture-based treatment selection) to facilitate targeted management for different parts of a dairy herd. In chapter 5, a deep recurrent neural network (RNN) model was used to detect the onset of CM using regularly collected data from AMS, and chapter 6 demonstrated that predictive performance of deep RNNs is robust to the unmeasured heterogeneity in data collected from multiple farms. Chapter 6 describes how immune response differs between children with abdominal pain symptomatic of appendicitis and provides evidence that data from a multiplex immunoassay conducted on admission may be used to effectively predict disease outcomes. In chapter 7, a similar multiplex immunoassay is used to explore associations between inflammation and diet using food records from patients with Crohn’s disease and demonstrates some of the statistical challenges encountered when working with multiple outcomes and large numbers of explanatory variables.
Open Access
Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study
(2020-09-15) Kosowan, Leanne; Page, John; Protudjer, Jennifer; Williamson, Tyler; Queenan, John; Singer, Alexander
Abstract Background Complete growth measurements are an essential part of pediatric care providing a proxy for a child’s overall health. This study describes the frequency of well-child visits, documented growth measurements, and clinic and provider factors associated with measurement. Methods Retrospective cross-sectional study utilizing electronic medical records (EMRs) from primary care clinics between 2015 and 2017 in Manitoba, Canada. This study assessed the presence of recorded height, weight and head circumference among children (0–24 months) who visited one of 212 providers participating in the Manitoba Primary Care Research Network. Descriptive and multivariable logistic regression analyses assessed clinic, provider, and patient factors associated with children having complete growth measurements. Results Our sample included 4369 children. The most frequent growth measure recorded was weight (79.2% n = 3460) followed by height (70.8% n = 3093) and head circumference (51.4% n = 2246). 67.5% of children (n = 2947) had at least one complete growth measurement recorded (i.e. weight, height and head circumference) and 13.7% (n = 599) had complete growth measurements at all well-child intervals attended. Pediatricians had 2.7 higher odds of documenting complete growth measures within well-child intervals compared to family physicians (95% CI 1.8–3.8). Additionally, urban located clinics (OR 1.7, 95% CI 1.2–2.5), Canadian trained providers (OR 2.3, 95% CI 1.4–3.7), small practice size (OR 1.6, 95% CI 1.2–2.2) and salaried providers (OR 3.4, 95% CI 2.2–5.2) had higher odds of documented growth measures. Conclusions Growth measurements are recorded in EMRs but documentation is variable based on clinic and provider factors. Pediatric growth measures at primary care appointments can improve primary prevention and surveillance of child health outcomes.
Open Access
Control of glycemia and blood pressure in British adults with diabetes mellitus and subsequent therapy choices: a comparison across health states
(2018-02-12) McAlister, Finlay A; Lethebe, Brendan C; Lambe, Caitlin; Williamson, Tyler; Lowerison, Mark
Abstract Background To examine the intensity of glycemic and blood pressure control in British adults with diabetes mellitus and whether control levels or treatment deintensification rates differ across health states. Methods Retrospective cohort study using primary care electronic medical records (the United Kingdom Health Improvement Network Database) for adults with diabetes diagnosed at least 6 months before the index HbA1C and systolic blood pressure (SBP) measurements (to give their primary care physicians time to achieve treatment goals). We used prescribing records for 6 months pre/post the index measurements to determine who had therapy subsequently deintensified (based on “glycemic therapy score” and “antihypertensive therapy score” derived from number and dosage of medications). Results Of 292,170 individuals with diabetes, HbA1C < 6% or SBP < 120 mmHg after at least 6 months of management was less common in otherwise fit patients (15.0 and 12.7%) than in those who were mildly frail (16.6 and 13.2%) or moderately–severely frail (20.2 and 17.0%, both p < 0.0001). In the next 6 months, only 44.7% of those with HbA1C < 6% had glycemic therapy reduced (44.4% of fit, 47.1% of mildly frail, and 41.5% of moderate-severely frail patients) and 39.8% of those with SBP < 120 had their antihypertensives decreased (39.3% of fit, 43.0% of mildly frail, and 46.7% of moderate-severely frail patients). On the other hand, more individuals exhibited higher than recommended levels for HbA1C or SBP after the first 6 months of therapy (37.3, 33.4, and 31.3% of fit, mildly frail, and moderately–severely frail patients had HbA1C > 7.5% and 46.6, 51.4, and 48.5% had SBP > 140 mmHg). The proportions of patients with HbA1C or SBP out of recommended treatment ranges changed little 6 months later despite frequent (median 14 per year) primary care visits. Conclusions Glycemic and hypertensive control exhibited statistically significant but small magnitude differences across frailty states. Medication deintensification was uncommon, even in frail patients below SBP and HbA1C targets. SBP levels were more likely to be outside recommended treatment ranges than glycemic levels. Trial registration As this study is a retrospective secondary analysis of electronic medical record data and not a health care intervention trial it was not registered
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, Sue
Open Access
Creating a Frailty Case Definition for Primary Care EMR Using Machine Learning
(2021-05-04) Aponte-Hao, Zhi Yun (Sylvia); Williamson, Tyler; Lee, Joon; McBrien, Kerry; Ronksley, Paul
Background: Frailty is a geriatric syndrome characterized by increased vulnerability and increased risk of adverse events. The Clinical Frailty Scale (CFS) is a judgement-based scale used to identify frailty in senior populations (over the age of 65). Primary care electronic medical records (EMRs) contain routinely collected medical data and can be used for frailty screening. There is currently no method to detect frailty automatically using primary care electronic medical records that aligns with the CFS definition. Purpose: To create a machine learning based algorithm for the identification of frailty in routinely collected primary care electronic medical records. Methods: Primary care physicians within the Canadian Primary Care Sentinel Surveillance Network retrospectively identified frailty in 5466 senior patients from their own practice using the CFS, and the corresponding patient EMR data were extracted and processed as features. The patient data were split 30-70, with 30% being the hold-out set used for final testing and 70% for the training set. A collection of machine learning algorithms was created using the training dataset, including regularized logistic regression models, support vector machines, random forests, k-nearest neighbours, classification and regression trees, feedforward neural networks, Naïve Bayes, and XGBoost. A balanced training dataset was also created by oversampling. Sensitivity analyses were also performed using two alternative dichotomization cut-offs of frailty. Final model performance was assessed using the hold-out dataset, and reported using ROC, accuracy, F1-score, sensitivity, specificity, positive and negative predictive values. Results: 18.4% of patients were classified as frail based on a CFS score of 5 and above. Of the 8 models developed, an XGBoost model had the best classification performance, with sensitivity of 78.14% and specificity of 74.41%. Neither the balanced training dataset, nor the sensitivity analyses using two alternative cut-offs resulted in improved performance. Conclusion: Supervised machine learning was able to distinguish between frail and non-frail patients with good performance. Future work may wish to develop a protocol for standardized assignment of the CFS, use all available unstructured and structured data, and supplement with additional geriatric-specific data.
Open Access
Developing a Data Integrated COVID-19 Tracking System for Decision-Making and Public Use
(International Journal of Popular Data Science, 2020-09-28) Krusina, Alexander; Chen, Oscar; Otero Varela, Lucia; Doktorchik, Chelsea; Avati, Vince; Knudsen, Søren; Southern, Danielle; Eastwood, Cathy; Sharma, Nishan; Williamson, Tyler
Introduction The unprecedented COVID-19 pandemic unveiled a strong need for advanced and informative surveillance tools. The Centre for Health Informatics (CHI) at the University of Calgary took action to develop a surveillance dashboard, which would facilitate the education of the public, and answer critical questions posed by local and national government. Objectives The objective of this study was to create an interactive method of surveillance, or a “COVID-19 Tracker” for Canadian use. The Tracker offers user-friendly graphics characterizing various aspects of the current pandemic (e.g. case count, testing, hospitalizations, and policy interventions). Methods Six publicly available data sources were used, and were selected based on the frequency of updates, accuracy and types of data, and data presentation. The datasets have different levels of granularity for different provinces, which limits the information that we are able to show. Additionally, some datasets have missing entries, for which the “last observation carried forward” method was used. The website was created and hosted online, with a backend server, which is updated on a daily basis. The Tracker development followed an iterative process, as new figures were added to meet the changing needs of policy-makers. Results The resulting Tracker is a dashboard that visualizes real-time data, along with policy interventions from various countries, via user-friendly graphs with a hover option that reveals detailed information. The interactive features allow the user to customize the figures by jurisdiction, country/region, and the type of data shown. Data is displayed at the national and provincial level, as well as by health regions. Conclusions The COVID-19 Tracker offers real-time, detailed, and interactive visualizations that have the potential to shape crucial decision-making and inform Albertans and Canadians of the current pandemic.
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, Sue
Abstract 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.
Open Access
Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study
(2023-09-02) Wu, Guosong; Cheligeer, Cheligeer; Southern, Danielle A.; Martin, Elliot A.; Xu, Yuan; Leal, Jenine; Ellison, Jennifer; Bush, Kathryn; Williamson, Tyler; Quan, Hude; Eastwood, Cathy A.
Abstract Background Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. Methods This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. Results There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90). Conclusions Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.
Open Access
Group B streptococcus (GBS) is an important pathogen in human disease- but what about in cystic fibrosis?
(2017-10-02) Skolnik, Kate; Nguyen, Austin; Thornton, Christina S; Waddell, Barbara; Williamson, Tyler; Rabin, Harvey R; Parkins, Michael D
Abstract Background Group B Streptococcus (GBS) is a common commensal capable of causing severe invasive infections. Most GBS infections occur in neonates (often as pneumonia). GBS can also cause infection in adults with diabetes and other immunological impairments but rarely leads to pneumonia in adults. GBS has occasionally been found in the sputum of Cystic Fibrosis (CF) patients, an inherited condition known for progressive lung disease. However, the epidemiology and clinical significance of GBS in CF are not understood. Methods We retrospectively reviewed a large single-centre adult CF population with an associated comprehensive, prospectively collected bacterial biobank beginning in 1978. We identified all individuals with GBS isolated from their sputum on at least one occasion. The primary outcome was risk of pulmonary exacerbation (PEx) at the time of the first GBS isolate compared to the preceding visit. Secondary outcomes included determining: prevalence of GBS infection in a CF population, whether GBS infections where transient or persistent, whether GBS strains were shared among patients, change in % predicted FEV1 at the time of GBS isolate compared to the preceding visit, PEx frequency after the first GBS isolate, change in % predicted FEV1 after the first GBS isolate, and complications of GBS infection. Results GBS was uncommon, infecting 3.5% (11/318) adults within our cohort. Only three individuals developed persistent GBS infection, all lasting > 12 months. There were no shared GBS strains among patients. PEx risk was not increased at initial GBS isolation (RR 5.0, CI 0.69–36.1, p=0.10). In the two years preceding initial GBS isolation compared to the two following years, there was no difference in PEx frequency (median 2, range 0–4 vs 1, range 0 to 5, respectively, p=0.42) or lung function decline, as measured by % predicted FEV1, (median −1.0%, range −19 to 7% vs median −6.0%, range −18 to 22%, p=0.86). There were no invasive GBS infections. Conclusion In adults with CF, GBS is uncommon and is generally a transient colonizer of the lower airways. Despite the presence of structural lung disease and impaired innate immunity in CF, incident GBS infection did not increase PEx risk, PEx frequency, rate of lung function decline, or other adverse clinical outcomes.
Open Access
Influence of dietary antioxidant and oxidant intake on leukocyte telomere length
(2017) Mickle, Alexis Tory; Friedenreich, Christine Marthe; Brenner, Darren; Beattie, Tara; Williamson, Tyler
Background: Telomeric DNA is highly susceptible to oxidative damage, and dietary habits may impact telomere attrition rates through the mediation of oxidative stress and chronic inflammation. Objectives: To examine the association between both the Dietary Inflammatory Index 2010 (DII) and the Alternative Healthy Eating Index 2010 (AHEI) with relative Leukocyte Telomere Length. Design: We conducted a cross-sectional analysis using baseline data from 301 healthy, inactive post-menopausal women. Diet quality was estimated using DII and AHEI scores derived from food frequency questionnaire data. LTL was measured using qPCR. Associations were examined using multivariable adjusted linear regression. Results: No statistically significant associations were detected between AHEI (p=0.20) or DII (p=0.91) and LTL in multivariable adjusted models. Conclusions: AHEI or DII scores were not related to LTL in this population. Future research is warranted to enhance understanding regarding the molecular processes involved in the relations between diet, telomere length and chronic disease risk.
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, 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).
Open Access
Preterm Birth: Understanding Temporal Changes in Anxiety and Depression Measures
(2017) Doktorchik, Chelsea; Premji, Shahirose; Patten, Scott; Slater, Donna; Williamson, Tyler
Background: This study aimed to understand whether there is a pattern of change in levels of anxiety and depression between the second and third trimesters of pregnancy that are associated with a risk of PTB. Chronic stress was assessed as a potential modifier of the relationship. Methods: This study conducted a secondary data analysis on the All Our Babies prospective cohort. Logistic regression modeling was used to analyze the data. Results: A worsening of anxiety during pregnancy increased the odds of preterm delivery (OR 2.70, 95% CI 1.28, 5.69; p=0.009). An improvement in anxiety reduced the odds of PTB (OR 0.96, 95% CI 0.94, 0.98; p=<0.001). Consistently low depression decreased the odds of PTB (OR 0.65, 95% CI 0.45, 0.96; p=0.029). Chronic stress did not modify any of these relationships. Conclusions: Efforts should be made to replicate these results in a cohort with a larger sample size.
Open Access
Re-Purposing the Ordering of Routine Laboratory Tests in Hospitalized Medical Patients (RePORT): protocol for a multicenter stepped-wedge cluster randomised trial to evaluate the impact of a multicomponent intervention bundle to reduce laboratory test over-utilization
(2024-07-02) Ambasta, Anshula; Holroyd-Leduc, Jayna M.; Pokharel, Surakshya; Mathura, Pamela; Shih, Andrew W.; Stelfox, Henry T.; Ma, Irene; Harrison, Mark; Manns, Braden; Faris, Peter; Williamson, Tyler; Shukalek, Caley; Santana, Maria; Omodon, Onyebuchi; McCaughey, Deirdre; Kassam, Narmin; Naugler, Chris
Abstract Background Laboratory test overuse in hospitals is a form of healthcare waste that also harms patients. Developing and evaluating interventions to reduce this form of healthcare waste is critical. We detail the protocol for our study which aims to implement and evaluate the impact of an evidence-based, multicomponent intervention bundle on repetitive use of routine laboratory testing in hospitalized medical patients across adult hospitals in the province of British Columbia, Canada. Methods We have designed a stepped-wedge cluster randomized trial to assess the impact of a multicomponent intervention bundle across 16 hospitals in the province of British Columbia in Canada. We will use the Knowledge to Action cycle to guide implementation and the RE-AIM framework to guide evaluation of the intervention bundle. The primary outcome will be the number of routine laboratory tests ordered per patient-day in the intervention versus control periods. Secondary outcome measures will assess implementation fidelity, number of all common laboratory tests used, impact on healthcare costs, and safety outcomes. The study will include patients admitted to adult medical wards (internal medicine or family medicine) and healthcare providers working in these wards within the participating hospitals. After a baseline period of 24 weeks, we will conduct a 16-week pilot at one hospital site. A new cluster (containing approximately 2–3 hospitals) will receive the intervention every 12 weeks. We will evaluate the sustainability of implementation at 24 weeks post implementation of the final cluster. Using intention to treat, we will use generalized linear mixed models for analysis to evaluate the impact of the intervention on outcomes. Discussion The study builds upon a multicomponent intervention bundle that has previously demonstrated effectiveness. The elements of the intervention bundle are easily adaptable to other settings, facilitating future adoption in wider contexts. The study outputs are expected to have a positive impact as they will reduce usage of repetitive laboratory tests and provide empirically supported measures and tools for accomplishing this work. Trial Registration This study was prospectively registered on April 8, 2024, via ClinicalTrials.gov Protocols Registration and Results System (NCT06359587). https://classic.clinicaltrials.gov/ct2/show/NCT06359587?term=NCT06359587&recrs=ab&draw=2&rank=1
Open Access
Sleeping for two: study protocol for a randomized controlled trial of cognitive behavioral therapy for insomnia in pregnant women
(2021-08-12) MacKinnon, Anna L.; Madsen, Joshua W.; Dhillon, Ashley; Keys, Elizabeth; Giesbrecht, Gerald F.; Williamson, Tyler; Metcalfe, Amy; Campbell, Tavis; Mrklas, Kelly J.; Tomfohr-Madsen, Lianne
Abstract Background Insomnia and sleep disturbances are common in pregnancy and have potentially significant consequences for both maternal and infant health. There is limited research examining the effectiveness of cognitive behavioral therapy for insomnia (CBT-I) during pregnancy. With increased distress and limited access to services during the COVID-19 pandemic, there is also an unprecedented need for telehealth delivery of treatment programs for pregnant women. The aims of this trial are to evaluate the impact of the Sleeping for Two adaptation of CBT-I in pregnancy (in-person or telehealth) versus treatment as usual (TAU) in reducing symptoms of insomnia (primary outcome), as well as increasing gestational length and reducing symptoms of depression (secondary outcomes). Methods A two-arm, single-blinded, parallel group randomized controlled trial (RCT) design with repeated measures will be used to evaluate the impact of CBT-I compared to TAU among a sample of 62 pregnant women, enrolled between 12 and 28 weeks of gestation, who self-identify as experiencing insomnia. Five weekly individual sessions of CBT-I will be delivered in person or via telehealth depending on physical distancing guidelines. Assessment of insomnia diagnosis by structured interview, self-reported insomnia symptom severity and sleep problems, and sleep quantity and quality as measured by a daily diary and actigraphy will occur at 12–28 weeks of pregnancy (T1), 1 week post-treatment (T2), and 6 months postpartum (T3). Discussion CBT-I delivered in pregnancy has the potential to reduce symptoms of insomnia and depression and could lead to reduced risk of preterm birth, all of which can minimize risk of negative maternal and child health and developmental consequences in the short (e.g., infant death) and long terms (e.g., developmental delays). This RCT builds on a successful open pilot trial conducted by our team and will provide further evaluation of a novel evidence-based treatment for pregnancy-related insomnia, which can be widely disseminated and used to treat individuals that are most in need of intervention. Findings will enhance understanding of pregnancy-related sleep problems, as well as means by which to improve the health and sleep of mothers and their children. Trial registration ClinicalTrials.gov NCT03918057. Registered on 17 April 2019.
Open Access
Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty
(2021-11-12) Thandi, Manpreet; Wong, Sabrina T.; Aponte-Hao, Sylvia; Grandy, Mathew; Mangin, Dee; Singer, Alexander; Williamson, Tyler
Abstract Background Practice based research and learning networks (PBRLNs) are groups of learning communities that focus on improving delivery and quality of care. Accurate data from primary care electronic medical records (EMRs) is crucial in forming the backbone for PBRLNs. The purpose of this work is to: (1) report on descriptive findings from recent frailty work, (2) describe strategies for working across PBRLNs in primary care, and (3) provide lessons learned for engaging PBRLNs. Methods We carried out a participatory based descriptive study that engaged five different PBRLNs. We collected Clinical Frailty Scale scores from a sample of participating physicians within each PBRLN. Descriptive statistics were used to analyze frailty scores and patients’ associated risk factors and demographics. We used the Consolidated Framework for Implementation Research to inform thematic analysis of qualitative data (meeting minutes, notes, and conversations with co-investigators of each network) in recognizing challenges of working across networks. Results One hundred nine physicians participated in collecting CFS scores across the five provinces (n = 5466). Percentages of frail (11-17%) and not frail (82-91%) patients were similar in all networks, except Ontario who had a higher percentage of frail patients (25%). The majority of frail patients were female (65%) and had a significantly higher prevalence of hypertension, dementia, and depression. Frail patients had more prescribed medications and numbers of healthcare encounters. There were several noteworthy challenges experienced throughout the research process related to differences across provinces in the areas of: numbers of stakeholders/staff involved and thus levels of burden, recruitment strategies, data collection strategies, enhancing engagement, and timelines. Discussion Lessons learned throughout this multi-jurisdictional work included: the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces. Conclusion The differences noted across CPCSSN networks in our frailty study highlight the challenges of multi-jurisdictional work across provinces and the need for consistent and collaborative healthcare planning efforts.
Open Access
Urban design and cardio-metabolic risk factors
(Elsevier, 2023-05-19) Koohsari, Mohammad Javad; Oka, Koichiro; Nakaya, Tomoki; Vena, Jennifer; Williamson, Tyler; Quan, Hude; McCormack, Gavin R.
Accumulating evidence suggests that the built environment may be associated with cardiovascular disease via its influence on health behaviours. The aim of this study was to estimate the associations between traditional and novel neighbourhood built environment metrics and clinically assessed cardio-metabolic risk factors among a sample of adults in Canada. A total of 7,171 participants from Alberta’s Tomorrow Project living in Alberta, Canada, were included. Cardio-metabolic risk factors were clinically measured. Two composite built environment metrics of traditional walkability and space syntax walkability were calculated. Among men, space syntax walkability was negatively associated with systolic and diastolic blood pressure (b=-0.87, 95% CI - 1.43, -0.31 and b=-0.45, 95% CI -0.86, -0.04, respectively). Space syntax walkability was also associated with lower odds of overweight/obese among women and men (OR=0.93, 95% CI 0.87, 0.99 and OR=0.88, 95% CI 0.79, 0.97, respectively). No significant associations were observed between traditional walkability and cardiometabolic outcomes. This study showed that the novel built environment metric based on the space syntax theory was associated with some cardio-metabolic risk factors.
Open Access
Validation of a case definition for depression in administrative data against primary chart data as a reference standard
(2019-01-07) Doktorchik, Chelsea; Patten, Scott; Eastwood, Cathy; Peng, Mingkai; Chen, Guanmin; Beck, Cynthia A; Jetté, Nathalie; Williamson, Tyler; Quan, Hude
Abstract Background Because the collection of mental health information through interviews is expensive and time consuming, interest in using population-based administrative health data to conduct research on depression has increased. However, there is concern that misclassification of disease diagnosis in the underlying data might bias the results. Our objective was to determine the validity of International Classification of Disease (ICD)-9 and ICD-10 administrative health data case definitions for depression using review of family physician (FP) charts as the reference standard. Methods Trained chart reviewers reviewed 3362 randomly selected charts from years 2001 and 2004 at 64 FP clinics in Alberta (AB) and British Columbia (BC), Canada. Depression was defined as presence of either: 1) documentation of major depressive episode, or 2) documentation of specific antidepressant medication prescription plus recorded depressed mood. The charts were linked to administrative data (hospital discharge abstracts and physician claims data) using personal health numbers. Validity indices were estimated for six administrative data definitions of depression using three years of administrative data. Results Depression prevalence by chart review was 15.9–19.2% depending on year, region, and province. An ICD administrative data definition of ‘2 depression claims with depression ICD codes within a one-year window OR 1 discharge abstract data (DAD) depression diagnosis’ had the highest overall validity, with estimates being 61.4% for sensitivity, 94.3% for specificity, 69.7% for positive predictive value, and 92.0% for negative predictive value. Stratification of the validity parameters for this case definition showed that sensitivity was fairly consistent across groups, however the positive predictive value was significantly higher in 2004 data compared to 2001 data (78.8 and 59.6%, respectively), and in AB data compared to BC data (79.8 and 61.7%, respectively). Conclusions Sensitivity of the case definition is often moderate, and specificity is often high, possibly due to undercoding of depression. Limitations to this study include the use of FP charts data as the reference standard, given the potential for missed or incorrect depression diagnoses. These results suggest that that administrative data can be used as a source of information for both research and surveillance purposes, while remaining aware of these limitations.