Variation in Testing for and Incidence of Celiac Autoimmunity in Canada: A Population-Based Study.

BACKGROUND & AIMS: The incidence of biopsy-confirmed celiac disease has increased. However, few studies have explored the incidence of celiac autoimmunity based on positive serology results. METHODS: A population-based cohort study assessed testing of tissue transglutaminase antibodies (tTG-IgA) in Alberta from 2012 to 2020. After excluding prevalent cases, incident celiac autoimmunity was defined as the first positive tTG-IgA result between 2015 and 2020. Testing and incidence rates for celiac autoimmunity were calculated per 1000 and 100,000 person-years, respectively. Incidence rate ratios (IRRs) were calculated to identify differences by demographic and regional factors. Average annual percent changes (AAPCs) assessed trends over time. RESULTS: The testing rate of tTG-IgA was 20.2 per 1000 person-years and remained stable from 2012 to 2020 (AAPC, 1.2%; 95% confidence interval [CI], -0.5 to 2.9). Testing was higher in female patients (IRR, 1.66; 95% CI, 1.65-1.66), those living in metropolitan areas (IRR, 1.39; 95% CI, 1.38-1.40), and in areas of lower socioeconomic deprivation (lowest compared to highest IRR, 1.24; 95% CI, 1.23-1.25). Incidence of celiac autoimmunity was 33.8 per 100,000 person-years and increased from 2015 to 2020 (AAPC, 6.2%; 95% CI, 3.1-9.5). Among those with tTG-IgA results ≥10 times the upper limit of normal, the incidence was 12.9 per 100,000 person-years. The incidence of celiac autoimmunity was higher in metropolitan settings (IRR, 1.28; 95% CI, 1.21-1.35) and in the least socioeconomically deprived areas compared to the highest (IRR, 1.22; 95% CI, 1.14-1.32). CONCLUSIONS: Incidence of celiac autoimmunity is high and increasing, despite stable testing rates. Variation in testing patterns may lead to underreporting the incidence of celiac autoimmunity in nonmetropolitan areas and more socioeconomically deprived neighborhoods.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada. METHODS: The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians. RESULTS: A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters. CONCLUSION: Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Urban design and cardio-metabolic risk factors.

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 7171 participants from Albertas 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 cardio-metabolic outcomes. This study showed that the novel built environment metric based on the space syntax theory was associated with some cardio-metabolic risk factors.

Uptake of pediatric patient-reported outcome and experience measures and challenges associated with their implementation in Alberta: a mixed-methods study.

BACKGROUND: Implementing Patient-reported Outcome Measures (PROMs) and Patient-reported Experience Measures (PREMs) is an effective way to deliver patient- and family-centered care (PFCC). Although Alberta Health Services (AHS) is Canada's largest and fully integrated health system, PROMs and PREMs are yet to be routinely integrated into the pediatric healthcare system. This study addresses this gap by investigating the current uptake, barriers, and enablers for integrating PROMs and PREMs in Alberta's pediatric healthcare system. METHODS: Pediatric clinicians and academic researchers with experience using PROMs and PREMs were invited to complete a quantitative survey. Additionally, key stakeholders were qualitatively interviewed to understand current challenges in implementing pediatric PROMs and PREMs within AHS. Quantitative data gathered from 22 participants were descriptively analyzed, and qualitative data from 14 participants were thematically analyzed. RESULTS: Participants identified 33 PROMs and 6 PREMs showing diversity in the types of pediatric PROMs and PREMs currently being used in Alberta and their mode of administration. The qualitatively identified challenges were associated with patients, family caregivers, and clinicians. The absence of system-level support, such as integration within electronic medical records, is considered a significant system-level challenge. CONCLUSIONS: The significant variation in the types of PROMs and PREMs used, the rationale for their use, and their mode of administration demonstrate the diverse and sporadic use of these measures in Alberta. These findings highlight the need for province-wide uniform implementation of pediatric PROMs and PREMs in Alberta. Our results could benefit healthcare organizations in developing evidence-based PROM and PREM implementation strategies in pediatrics.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.

BACKGROUND: Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research. OBJECTIVE: The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences. METHODS: We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results. RESULTS: We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized. CONCLUSIONS: The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Association of co-morbidity with acute stroke mortality by age and time since stroke: A population-based study.

OBJECTIVE: To examine whether the association of co-morbidity with mortality after acute stroke is influenced by stroke type, age, sex, or time since stroke. MATERIALS AND METHODS: We conducted a province-wide population-based study using linked administrative databases to identify all admissions for acute stroke between 2007-2018 in Alberta, Canada. We used Cox proportional hazard models to determine the association of severe co-morbidity based on the Charlson Co-morbidity Index with 1-year mortality after stroke, assessing for effect modification by stroke type, age, and sex, and with adjustment for estimated stroke severity, comprehensive stroke centre care, hypertension, atrial fibrillation, and year of study. We used a piecewise model to analyze the impact of co-morbidity across four time periods. RESULTS: We had 28,672 patients in our final cohort (87.8% ischemic stroke). The hazard of mortality with severe co-morbidity was higher for individuals with ischemic stroke (adjusted hazard ratio [aHR] 2.20, 95% CI 2.07-2.32) compared to those with intracerebral hemorrhage (aHR 1.70, 95% CI 1.51-1.92; pint<0.001), and higher in individuals under age 75 (aHR 3.20, 95% CI 2.90-3.53) compared to age ≥75 (aHR 1.93, 95% CI 1.82-2.05, pint<0.001). There was no interaction by sex. The hazard ratio increased in a graded fashion at younger ages and was higher after the first 30 days of acute stroke. CONCLUSION: There was a stronger association between co-morbidity and mortality at younger age and in the subacute phase of stroke. Further research is needed to determine the reason for these findings and identify ways to improve outcomes among those with stroke and co-morbid conditions at young age.

Associations Between Physician Prescribing Behavior and Persistent Postoperative Opioid Use Among Cancer Patients Undergoing Curative-intent Surgery: A Population-based Cohort Study.

OBJECTIVE: This study aimed to evaluate the association between prescribers' opioid prescribing history and persistent postoperative opioid use in cancer patients undergoing curative-intent surgery. BACKGROUND: Study has shown that patients may be over-prescribed analgesics after surgery. However, whether and how the prescriber's opioid prescribing behavior impacts persistent opioid use is unclear. METHODS: All adults with a diagnosis of solid cancers who underwent surgery during the study period (2009-2015) in Alberta, Canada and were opioid-naïve were included. The key exposure was the historical opioid-prescribing pattern of a patient's most responsible prescriber. The primary outcome was "new persistent postoperative opioid user," was defined as a patient who was opioid-naïve before surgery and subsequently filled at least 1 opioid prescription between 60 and 180 days after surgery. RESULTS: We identified 24,500 patients. Of these, 2106 (8.6%) patients became a new persistent opioid user after surgery. Multivariate analysis demonstrated that patients with most responsible prescribers that historically prescribed higher daily doses of opioids (≥50 vs <50 mg oral morphine equivalent) had an increased risk of new persistent opioid use after surgery (odds ratio = 2.41, P < 0.0001). In addition to the provider's prescribing pattern, other factors including younger age, comorbidities, presurgical opioid use, chemotherapy, type of tumor/surgical procedure were also found to be independently associated with new persistent postoperative opioid use. CONCLUSIONS: Our results suggest that prescriber with a history of prescribing a higher opioid dose is an important predictor of persistent postoperative opioid use among cancer patients undergoing curative-intent surgery.

A Multicenter Cohort Study of Falls Among Patients Admitted to the ICU.

OBJECTIVES: To determine the incidence of falls, risk factors, and adverse outcomes, among patients admitted to the ICU. DESIGN: Retrospective cohort study. SETTING: Seventeen ICUs in Alberta, Canada. PATIENTS: Seventy-three thousand four hundred ninety-five consecutive adult patient admissions between January 1, 2014, and December 31, 2019. MEASUREMENTS AND MAIN RESULTS: A mixed-effects negative binomial regression model was used to examine risk factors associated with falls. Linear and logistic regression models were used to evaluate adverse outcomes. Six hundred forty patients experienced 710 falls over 398,223 patient days (incidence rate of 1.78 falls per 1,000 patient days [95% CI, 1.65-1.91]). The daily incidence of falls increased during the ICU stay (e.g., day 1 vs day 7; 0.51 vs 2.43 falls per 1,000 patient days) and varied significantly between ICUs (range, 0.37-4.64 falls per 1,000 patient days). Male sex (incidence rate ratio [IRR], 1.37; 95% CI, 1.15-1.63), previous invasive mechanical ventilation (IRR, 1.82; 95% CI, 1.40-2.38), previous sedative and analgesic medication infusions (IRR, 1.60; 95% CI, 1.15-2.24), delirium (IRR, 3.85; 95% CI, 3.23-4.58), and patient mobilization (IRR, 1.26; 95% CI, 1.21-1.30) were risk factors for falling. Falls were associated with longer ICU (ratio of means [RM], 3.10; 95% CI, 2.86-3.36) and hospital (RM, 2.21; 95% CI, 2.01-2.42) stays, but lower odds of death in the ICU (odds ratio [OR], 0.09; 95% CI, 0.05-0.17) and hospital (OR, 0.21; 95% CI, 0.14-0.30). CONCLUSIONS: We observed that among ICU patients, falls occur frequently, vary substantially between ICUs, and are associated with modifiable risk factors, longer ICU and hospital stays, and lower risk of death. Our study suggests that fall prevention strategies should be considered for critically ill patients admitted to ICU.

Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning.

BACKGROUND: Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. METHODS: Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. RESULTS: The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. CONCLUSIONS: This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.

Significant Regional Differences in Antibiotic Use Across 576 US Hospitals and 11 701 326 Adult Admissions, 2016-2017.

BACKGROUND: Quantifying the amount and diversity of antibiotic use in United States hospitals assists antibiotic stewardship efforts but is hampered by limited national surveillance. Our study aimed to address this knowledge gap by examining adult antibiotic use across 576 hospitals and nearly 12 million encounters in 2016-2017. METHODS: We conducted a retrospective study of patients aged ≥ 18 years discharged from hospitals in the Premier Healthcare Database between 1 January 2016 and 31 December 2017. Using daily antibiotic charge data, we mapped antibiotics to mutually exclusive classes and to spectrum of activity categories. We evaluated relationships between facility and case-mix characteristics and antibiotic use in negative binomial regression models. RESULTS: The study included 11 701 326 admissions, totaling 64 064 632 patient-days, across 576 hospitals. Overall, patients received antibiotics in 65% of hospitalizations, at a crude rate of 870 days of therapy (DOT) per 1000 patient-days. By class, use was highest among ß-lactam/ß-lactamase inhibitor combinations, third- and fourth-generation cephalosporins, and glycopeptides. Teaching hospitals averaged lower rates of total antibiotic use than nonteaching hospitals (834 vs 957 DOT per 1000 patient-days; P < .001). In adjusted models, teaching hospitals remained associated with lower use of third- and fourth-generation cephalosporins and antipseudomonal agents (adjusted incidence rate ratio [95% confidence interval], 0.92 [.86-.97] and 0.91 [.85-.98], respectively). Significant regional differences in total and class-specific antibiotic use also persisted in adjusted models. CONCLUSIONS: Adult inpatient antibiotic use remains high, driven predominantly by broad-spectrum agents. Better understanding reasons for interhospital usage differences, including by region and teaching status, may inform efforts to reduce inappropriate antibiotic prescribing.

Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results From 576 US Facilities.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission's antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals' utilization rankings.

A data quality assessment to inform hypertension surveillance using primary care electronic medical record data from Alberta, Canada.

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.

Distribution, management and outcomes of AMI according to principal diagnosis priority during inpatient admission.

BACKGROUND: In recent years, there has been a growing interest in outcomes of patients with acute myocardial infarction (AMI) using large administrative datasets. The present study was designed to compare the characteristics, management strategies and acute outcomes between patients with primary and secondary AMI diagnoses in a national cohort of patients. METHODS: All hospitalisations of adults (≥18 years) with a discharge diagnosis of AMI in the US National Inpatient Sample from January 2004 to September 2015 were included, stratified by primary or secondary AMI. The International Classification of Diseases, ninth revision and Clinical Classification Software codes were used to identify patient comorbidities, procedures and clinical outcomes. RESULTS: A total of 10 864 598 weighted AMI hospitalisations were analysed, of which 7 186 261 (66.1%) were primary AMIs and 3 678 337 (33.9%) were secondary AMI. Patients with primary AMI diagnoses were younger (median 68 vs 74 years, P < .001) and less likely to be female (39.6% vs 48.5%, P < .001). Secondary AMI was associated with lower odds of receipt of coronary angiography (aOR 0.19; 95%CI 0.18-0.19) and percutaneous coronary intervention (0.24; 0.23-0.24). Secondary AMI was associated with increased odds of MACCE (1.73; 1.73-1.74), mortality (1.71; 1.70-1.72), major bleeding (1.64; 1.62-1.65), cardiac complications (1.69; 1.65-1.73) and stroke (1.68; 1.67-1.70) (P < .001 for all). CONCLUSIONS: Secondary AMI diagnoses account for one-third of AMI admissions. Patients with secondary AMI are older, less likely to receive invasive care and have worse outcomes than patients with a primary diagnosis code of AMI. Future studies should consider both primary and secondary AMI diagnoses codes in order to accurately inform clinical decision-making and health planning.

Exploring the differences in ICD and hospital morbidity data collection features across countries: an international survey.

BACKGROUND: The International Classification of Diseases (ICD) is the reference standard for reporting diseases and health conditions globally. Variations in ICD use and data collection across countries can hinder meaningful comparisons of morbidity data. Thus, we aimed to characterize ICD and hospital morbidity data collection features worldwide. METHODS: An online questionnaire was created to poll the World Health Organization (WHO) member countries that were using ICD. The survey included questions focused on ICD meta-features and hospital data collection systems, and was distributed via SurveyMonkey using purposive and snowball sampling. Accordingly, senior representatives from organizations specialized in the topic, such as WHO Collaborating Centers, and other experts in ICD coding were invited to fill out the survey and forward the questionnaire to their peers. Answers were collated by country, analyzed, and presented in a narrative form with descriptive analysis. RESULTS: Responses from 47 participants were collected, representing 26 different countries using ICD. Results indicated worldwide disparities in the ICD meta-features regarding the maximum allowable coding fields for diagnosis, the definition of main condition, and the mandatory type of data fields in the hospital morbidity database. Accordingly, the most frequently reported answers were "reason for admission" as main condition definition (n = 14), having 31 or more diagnostic fields available (n = 12), and "Diagnoses" (n = 26) and "Patient demographics" (n = 25) for mandatory data fields. Discrepancies in data collection systems occurred between but also within countries, thereby revealing a lack of standardization both at the international and national level. Additionally, some countries reported specific data collection features, including the use or misuse of ICD coding, the national standards for coding or lack thereof, and the electronic abstracting systems utilized in hospitals. CONCLUSIONS: Harmonizing ICD coding standards/guidelines should be a common goal to enhance international comparisons of health data. The current international status of ICD data collection highlights the need for the promotion of ICD and the adoption of the newest version, ICD-11. Furthermore, it will encourage further research on how to improve and standardize ICD coding.

Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates.

QUESTION: Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards? FINDINGS: Applying a methodological framework on simulated PSI report card data, we compare the adjusted PSI rates of three hospitals with variable quality of data and coding. This framework combines (i) a measure of PSI rates using existing algorithms; (ii) a medical record review on a small random sample of charts to produce a measure of hospital-specific data validity and (iii) a simple Bayesian calculation to derive estimated true PSI rates. For example, the estimated true PSI rate, for a theoretical hospital with a moderately good quality of coding, could be three times as high as the measured rate (for example, 1.4% rather than 0.5%). For a theoretical hospital with relatively poor quality of coding, the difference could be 50-fold (for example, 5.0% rather than 0.1%). MEANING: Combining a medical chart review on a limited number of medical charts at the hospital level creates an approach to producing health system report cards with estimates of true hospital-level adverse event rates.

Incidence of Celiac Disease Is Increasing Over Time: A Systematic Review and Meta-analysis.

OBJECTIVES: To conduct a systematic review and meta-analysis that defines the worldwide incidence of celiac disease (CD) and examines temporal trends. METHODS: MEDLINE and EMBASE were searched for population-based studies reporting the incidence of CD in the overall population, children, or adults. No limits were placed on year or language of publication. Studies solely examining at-risk populations (e.g., patients with type 1 diabetes) were excluded. Random-effects models were performed to meta-analyze sex- and age-specific incidence in the 21st century. Temporal trend analyses assessed the average annual percent change in CD incidence over time. RESULTS: Of 11,189 citations, 86 eligible studies were identified for inclusion, of which 50 were deemed suitable for analyses. In the 21st century, the pooled female incidence of CD was 17.4 (95% confidence interval [CI]: 13.7, 21.1) (I = 99.5%) per 100,000 person-years, compared with 7.8 (95% CI: 6.3, 9.2) (I = 98.6%) in males. Child-specific incidence was 21.3 per 100,000 person-years (95% CI: 15.9, 26.7) (I = 99.7%) compared with 12.9 (95% CI: 7.6, 18.2) (I = 99.9%) in adults. Pooling average annual percent changes showed the incidence of CD to be increasing by 7.5% (95% CI: 5.8, 9.3) (I = 79.6%) per year over the past several decades. DISCUSSION: Incidence of CD is highest in females and children. Overall, the incidence has been significantly rising in the latter half of the 20th century and into the 21st century throughout the Western world. Population-based studies in Africa, Asia, and Latin America are needed to provide a comprehensive picture of the global incidence of CD.

Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.

BACKGROUND: Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms. This study aimed to enhance the ICD algorithm for HF definition by incorporating comprehensive information from EMRs. METHODS: The study included 2106 inpatients in Calgary, Alberta, Canada. Medical chart review was used as the reference gold standard for evaluating developed algorithms. The commonly used ICD codes for defining HF were used (namely, the ICD algorithm). The performance of different algorithms using the free text discharge summaries from a population-based EMR were compared with the ICD algorithm. These algorithms included a keyword search algorithm looking for HF-specific terms, a machine learning-based HF concept (HFC) algorithm, an EMR structured data based algorithm, and combined algorithms (the ICD and HFC combined algorithm). RESULTS: Of 2106 patients, 296 (14.1%) were patients with HF as determined by chart review. The ICD algorithm had 92.4% positive predictive value (PPV) but low sensitivity (57.4%). The EMR keyword search algorithm achieved a higher sensitivity (65.5%) than the ICD algorithm, but with a lower PPV (77.6%). The HFC algorithm achieved a better sensitivity (80.0%) and maintained a reasonable PPV (88.9%) compared with the ICD algorithm and the keyword algorithm. An even higher sensitivity (83.3%) was reached by combining the HFC and ICD algorithms, with a lower PPV (83.3%). The structured EMR data algorithm reached a sensitivity of 78% and a PPV of 54.2%. The combined EMR structured data and ICD algorithm had a higher sensitivity (82.4%), but the PPV remained low at 54.8%. All algorithms had a specificity ranging from 87.5% to 99.2%. CONCLUSIONS: Applying natural language processing and machine learning on the discharge summaries of inpatient EMR data can improve the capture of cases of HF compared with the widely used ICD algorithm. The utility of the HFC algorithm is straightforward, making it easily applied for HF case identification.

Adverse Events Among Hospitalized Critically Ill Patients: A Retrospective Cohort Study.

OBJECTIVE: The objective of this study was to estimate the frequency and type of adverse events (AEs) among critically ill patients and identify patient and hospital factors associated with AEs and clinical and health care utilization consequences of AEs. MATERIALS AND METHODS: This retrospective cohort study includes patients admitted to 30 intensive care units (ICUs) in Alberta, Canada from May 2014 to April 2017. The main outcome was AEs derived from validated ICD-10, Canadian code algorithms for 18 AEs. Estimates of the proportion and rate of AEs are presented. The association between documented AEs and patient (eg, age, sex, comorbidities) and hospital (eg, ICU site and type, length of stay, readmission) variables are described using regression methods. RESULTS: Of 49,447 hospital admissions with admission to ICU, ≥1 AEs were documented in 12,549 (25%) admissions. The most common AEs were respiratory complications (10%) and hospital-acquired infections (9%). AEs were associated with having ≥2 comorbidities [odds ratio (OR)=1.4, 95% confidence interval (CI)=1.3-1.4], being admitted to the ICU from the operating room or another hospital ward (OR=1.8, 95% CI=1.7-2.0 and OR=2.7, 95% CI=2.5-3.0, respectively) and being readmitted to ICU during their hospital stay (OR=4.8, 95% CI=4.7-5.6). Patients with an AE stayed 5.4 days longer in ICU (95% CI=5.2-5.6 d, P<0.001), 18.2 days longer in hospital (95% CI=17.7-18.8 d, P<0.001) and had increased odds of hospital mortality (OR=1.5, 95% CI=1.4-1.6) than those without an AE. CONCLUSIONS: AEs are common among critically ill patients and certain factors are associated with AEs. Documented AEs are associated with longer stays and increased mortality.

Psychometric evaluation of a Canadian version of the Seattle Angina Questionnaire (SAQ-CAN).

BACKGROUND: The Seattle Angina Questionnaire (SAQ) is a widely-used patient-reported outcomes measure in patients with heart disease. This study assesses the validity and reliability of the SAQ in a Canadian cohort of individuals with stable angina. METHODS AND RESULTS: Data are from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry, a population-based registry of patients who received cardiac catheterization in Alberta, Canada. The cohort consists of 4052 patients undergoing cardiac catheterization for stable angina and completed the SAQ within 2 weeks. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess the factorial structure of the SAQ. Internal and test-retest reliabilities of a new measure (i.e., SAQ-CAN) was measured using Cronbach α and intraclass correlation coefficient, respectively. CFA model fit was assessed using the root mean square error of approximation (RMSEA) and comparative fit index (CFI). Construct validity of the SAQ-CAN was assessed in relation to Hospital Anxiety and Depression Scales (HADS), Euro Quality of life 5 dimension (EQ5D), and original SAQ. Of the 4052 patients included in this analysis, 3281 (80.97%) were younger than 75 years old, while 3239 (79.94%) were male. Both exploratory and confirmatory factor analyses revealed a four-factorial structure consisting of 16 items that provided a better fit to the data (RMSEA = 0.049 [90% CI = (0.047, 0.052)]; CFI = 0.975). The 16-item SAQ demonstrated good to excellent internal reliability (Cronbach's α range from 0.77 to 0.90), moderate to strong correlation with the Original SAQ and EQ5D but negligible correlations with HADS. CONCLUSION: The SAQ-CAN has acceptable psychometric properties that are comparable to the original SAQ. We recommend its use for assessing coronary health outcomes in Canadian patients with Coronary Artery Disease.

Pregnancy complications and risk of preterm birth according to maternal age: A population-based study of delivery hospitalizations in Alberta.

INTRODUCTION: Pregnancy-related medical complications are associated with a 2- to 5-fold increased risk of preterm birth (PTB), but the nature of this etiologic relation in context with maternal factors remains poorly understood. Previous studies have generally treated maternal age as a confounder but overlooked its potential as an effect modifier, whereby the magnitude of the effect of complications on PTB could differ significantly across age groups. We investigated whether advanced maternal age (≥35 years) modified the association between pregnancy complications and PTB, and compared population-attributable fractions of PTB from complications in women older vs younger than 35 years. MATERIAL AND METHODS: We analyzed population-based, cross-sectional data from the Alberta Discharge Abstract Database for women aged 18-50 years with singleton live births in hospital between 2014 and 2017 (n = 152 246). Complications were preeclampsia, gestational diabetes, and placental disorders identified using diagnostic codes. Outcomes were spontaneous (sPTB) or iatrogenic (iPTB) PTB before 37 weeks of gestation. We estimated risk ratios and risk differences using modified Poisson and log binomial regression, respectively, adjusting for confounders (pregnancy history, comorbidities). Population-attributable fractions estimates were calculated from risk ratios. Age modification was tested using interaction terms and Z-tests. RESULTS: Prevalence of advanced maternal age was 19.2%. Pregnancy complications and s/iPTB were more common among women aged ≥35 years. Age modified the risk of PTB from preeclampsia only, with risk differences of 9.9% (95% CI 7.2%-12.6%) in older women vs 6.1% (95% CI 4.8%-7.4%) in younger women (P-interaction = 0.012) for sPTB, and 29.5% (95% CI 26.0%-33.1%) vs 20.8% (95% CI 18.9%-22.6%, P-interaction <0.001) for iPTB. Population-attributable fractions of s/iPTB types for all complications were consistently 2%-5% larger in women aged ≥35 years, and significantly larger for preeclampsia (sPTB: 5.1% vs 2.7%, P = 0.002; iPTB: 18.8% vs 14.0%, P < 0.001) and placental disorders (sPTB: 12.5% vs 8.7%, P < 0.001; iPTB: 13.2% vs 8.9%, P < 0.001). CONCLUSIONS: Of the pregnancy complications studied, advanced maternal age only modified the association between PTB and preeclampsia, such that older women with preeclampsia have a higher risk for s/iPTB than younger counterparts. Pregnancy complications contribute to a sizable proportion of PTBs in Alberta, especially among women aged ≥35 years. Findings may inform clinical risk assessment and population-level policy targeting PTB.