Defining opioid naïve and implications for monitoring opioid use: A population-based study in Alberta, Canada.

PURPOSE: Reducing initial exposure of "opioid naïve" patients to opioids is a public health priority. Identifying opioid naïve patients is difficult, as numerous definitions are used. The objective is to summarize current definitions and evaluate their impact on opioid naïve measures in Alberta. METHODS: An exploratory data analysis of the literature was conducted over the last 10 years to identify definitions commonly used in the literature to define opioid naïve. Then, using these definitions as a guide, we descriptively report the proportion of patients in Alberta between 2017 and 2021 who would be considered as opioid naïve using these definitions and all opioid dispensing data. RESULTS: Three categories of definitions were broadly identified: (1) no opioid use within the previous 30 days/6 months/1 year, based on dispensation date; (2) no opioid use based on dispensation date plus days of supply; and, (3) exclusion of codeine from Definitions 1 and 2. Applying these definitions to the Alberta population showed a very wide range in the proportion who would be considered as opioid naïve. Overall, 36.4% of Albertans (n = 1 551 075) had an opioid dispensation in 2017-2021. The average age was 46.6 ± 18.8 and 52.8% were female. The proportion of opioid naïve were most affected by the "opioid free" period, with 97.4%, 83.2%, and 65.6% being classified as opioid naïve using time windows from Definition 1 (30 days, 6 months, 1 year of no prior opioid use). Definitions 2 and 3 did not materially change the results. Further extending the "opioid free" period to 2 years showed only 35% were opioid naïve. CONCLUSIONS: The most convenient definition for "opioid naïve" was the use of an "opioid free" period. The choice of window would depend on how the information may be used to assistant in clinical decisions with longer windows more likely to reflect true opioid naïve patients. Irrespective of definition used, a large proportion of opioid users would be considered opioid naïve in Alberta.

Effectiveness of RADAR: An Innovative Model to Organize Diabetes Care in First Nations Communities.

Challenges exist for the management of diabetes care in First Nations populations. RADAR (Reorganizing the Approach to Diabetes through the Application of Registries) is a culturally appropriate, innovative care model that incorporates a disease registry and electronic health record for local care provision with remote coordination, tailored for First Nations people. This study assessed the effectiveness of RADAR on patient outcomes and diabetes care organization in participating communities in Alberta, Canada. It revealed significant improvements in outcomes after 2 years, with 91% of patients achieving a primary combined end point of a 10% improvement in or persistence at target for A1C, systolic blood pressure, and/or LDL cholesterol. Qualitative assessment showed that diabetes care organization also improved. These multimethod findings support tailored diabetes care practices in First Nations populations.

Predicting 30-Day Readmissions in Patients With Heart Failure Using Administrative Data: A Machine Learning Approach.

BACKGROUND: We sought to develop machine learning (ML) models trained on administrative data which predict risk of readmission in patients with heart failure and to evaluate and compare the ML model with the currently used LaCE score using clinically informative metrics. METHODS AND RESULTS: This prognostic study was conducted in Alberta, Canada, on 9845 patients with confirmed heart failure admitted to hospital between 2012 and 2019. The outcome was unplanned all-cause hospital readmission within 30 days of discharge. We used 80% of the data for the ML model development and 20% for independent validation. We reported, using the validation set, c-statistics (area under the receiver operating characteristic curves)and performance metrics (likelihood ratio, positive predictive values) for the XGBoost model and a modified LaCE score within their respective predictive thresholds. Boosted tree-based classifiers had higher area under the receiver operating characteristic curves (0.65 for XGBoost) compared with others (0.58 for neural networks) and 0.57 for the modified LaCE. Within the predicted threshold range of the XGBoost classifier, the positive likelihood ratio was 1.00 at the low end of predicted risk and 6.12 at the high end, resulting in a positive predictive value (post-test probability) range of 21%-62%; the pretest probability of readmission was 20.9% using prevalence. The corresponding positive likelihood ratios and positive predictive values across LaCE score thresholds were 1.00-1.20 and 21%-24%, respectively. CONCLUSIONS: Despite predicting readmissions better than the LaCE, even the best ML model trained on administrative health data (XGBoost) did not provide substantially informative prediction performance as it only generated a moderate shift from pre to post-test probability. Health systems wishing to deploy such a tool should consider training ML models with additional data. Adding other techniques like natural language processing, along with ML, to use other clinical information (like chart notes) might improve prediction performance.

Addressing the gaps in diabetes care in first nations communities with the reorganizing the approach to diabetes through the application of registries (RADAR): the project protocol.

BACKGROUND: Type-2 diabetes rates in First Nations communities are 3-5 times higher than the general Canadian population, resulting in a high burden of disease, complications and comorbidity. Limited community nursing capacity, isolated environments and a lack of electronic health records (EHR)/registries lead to a reactive, disorganized approach to diabetes care for many First Nations people. The Reorganizing the Approach to Diabetes through the Application of Registries (RADAR) project was developed in alignments with federal calls for innovative, culturally relevant, community-specific programs for people with type-2 diabetes developed and delivered in partnership with target communities. METHODS: RADAR applies both an integrated diabetes EHR/registry system (CARE platform) and centralized care coordinator (CC) service that will support local healthcare. The CC will work with local healthcare workers to support patient and community health needs (using the CARE platform) and build capacity in best practices for type-2 diabetes management. A modified stepped wedge controlled trial design will be used to evaluate the model. During the baseline phase, the CC will work with local healthcare workers to identify patients with type-2 diabetes and register them into the CARE platform, but not make any management recommendations. During the intervention phase, the CC will work with local healthcare workers to proactively manage patients with type-2 diabetes, including monitoring and recall of patients, relaying clinical information and coordinating care, facilitated through the shared use of the CARE platform. The RE-AIM framework will provide a comprehensive assessment of the model. The primary outcome measure will be a 10% improvement in any one of A1c, BP, or cholesterol over the baseline values. Secondary endpoints will address other diabetes care indicators including: the proportion of clinical measures completed in accordance with guidelines (e.g., foot and eye examination, receipt of vaccinations, smoking cessation counseling); the number of patients registered in CARE; and the proportion of patients linked to a health services provider. The cost-effectiveness of RADAR specific to these communities will be assessed. Concurrent qualitative assessments will provide contextual information, such as the quality/usability of the CARE platform and the impact/satisfaction with the model. DISCUSSION: RADAR combines innovative technology with personalized support to deliver organized diabetes care in remote First Nations communities in Alberta. By improving the ability of First Nations to systematically identify and track diabetes patients and share information seamlessly an overall improvement in the quality of clinical care of First Nations people living with type-2 diabetes on reserve is anticipated. TRIAL REGISTRATION: ISRCTN study ID ISRCTN14359671 , retrospectively registered October 7, 2016.

Effectiveness of RADAR on diabetes processes of care for First Nations communities in Alberta, Canada.

The epidemic of type-2 diabetes in First Nations communities is tragic. Culturally-appropriate approaches addressing multiple components, focusing beyond glycemic control, are urgently needed. Using an intention-to-treat framework, 13 processes of care indicators were assessed to compare proportions of patients who received care at baseline relative to 2-year follow-up. Clinical improvements were demonstrated across major process of care indicators (e.g. screening, education, and vaccination activities). We found RADAR improved reporting for most diabetes processes of care across seven FN communities and was effective in supporting diabetes care for FN communities, in Alberta Canada.

Predicting 30-day risk from benzodiazepine/Z-drug dispensations in older adults using administrative data: A prognostic machine learning approach.

OBJECTIVE: To develop a machine-learning (ML) model using administrative data to estimate risk of adverse outcomes within 30-days of a benzodiazepine (BZRA) dispensation in older adults for use by health departments/regulators. DESIGN, SETTING AND PARTICIPANTS: This study was conducted in Alberta, Canada during 2018-2019 in Albertans 65 years of age and older. Those with any history of malignancy or palliative care were excluded. EXPOSURE: Each BZRA dispensation from a community pharmacy served as the unit of analysis. MAIN OUTCOMES AND MEASURES: ML algorithms were developed on 2018 administrative data to predict risk of any-cause hospitalization, emergency department visit or death within 30-days of a BZRA dispensation. Validation on 2019 administrative data was done using XGBoost to evaluate discrimination, calibration and other relevant metrics on ranked predictions. Daily and quarterly predictions were simulated on 2019 data. RESULTS: 65,063 study participants were included which represented 633,333 BZRA dispensation during 2018-2019. The validation set had 314,615 dispensations linked to 55,928 all-cause outcomes representing a pre-test probability of 17.8%. C-statistic for the XGBoost model was 0.75. Measuring risk at the end of 2019, the top 0.1 percentile of predicted risk had a LR + of 40.31 translating to a post-test probability of 90%. Daily and quarterly classification simulations resulted in uninformative predictions with positive likelihood ratios less than 10 in all risk prediction categories. Previous history of admissions was ranked highest in variable importance. CONCLUSION: Developing ML models using only administrative health data may not provide health regulators with sufficient informative predictions to use as decision aids for potential interventions, especially if considering daily or quarterly classifications of BZRA risks in older adults. ML models may be informative for this context if yearly classifications are preferred. Health regulators should have access to other types of data to improve ML prediction.

Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data.

OBJECTIVE: To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions. DESIGN: This prognostic study used population-level administrative health data to develop an ML prediction model. SETTING: This study took place in Alberta, Canada during 2018-2019. PARTICIPANTS: Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded. EXPOSURE: Unit of analysis was the individual person. MAIN OUTCOMES/MEASURES: We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data. RESULTS: Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%-30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings. CONCLUSION: ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.

Appropriateness of COVID-19 public health guidelines for an Alberta First Nations community.

OBJECTIVES: The First Nations people experience significant challenges that may influence the ability to follow COVID-19 public health directives on-reserve. This study aimed to describe experiences, perceptions and circumstances of an Alberta First Nations community, related to COVID-19 public health advice. We hypothesized that many challenges ensued when following and implementing advice from public health experts. METHODS: With First Nations leadership and staff, an online cross-sectional survey was deployed between April 24 and June 25, 2020. It assessed the appropriateness of public health advice to curb COVID-19 within this large First Nations community. Both quantitative and qualitative data were captured and described. RESULTS: A total of 106 adults living on-reserve responded; over 80% were female. Difficulty accessing food was significant by employment status (p = 0.0004). Those people with lower income found accessing food (p = 0.0190) and getting essential medical care (p = 0.0060), clothing (p = 0.0280) and transportation (p = 0.0027) more difficult. Some respondents described lost income associated with COVID-19 experiences, as well as difficulties accessing essential supplies. Respondents found "proper handwashing" most easy (98%) and "keeping a distance of 2 m from others" most difficult (23%). Many respondents found following public health advice within their personal domain easy and put "family safety" first but experienced some difficulties when navigating social aspects and obligations, particularly when unable to control the actions of others. People stated wanting clear information, but were sometimes critical of the COVID-19 response. CONCLUSION: First Nations people face many additional challenges within the COVID-19 response, driven in part by ongoing issues related to significant societal, economic, and systemic factors.

RéSUMé: OBJECTIFS: Les Premiers Peuples connaissent d'importantes difficultés qui peuvent nuire à la capacité de suivre les directives de santé publique sur la COVID-19 dans les réserves. Notre étude visait à décrire les expériences, les perceptions et la situation d'une Première Nation de l'Alberta en lien avec les consignes de santé publique sur la COVID-19. Nous avons postulé que de nombreuses difficultés s'ensuivent lorsque les conseils des spécialistes de la santé publique sont suivis et appliqués. MéTHODE: Avec les dirigeants et le personnel de la Première Nation, nous avons mené un sondage transversal en ligne entre le 24 avril et le 25 juin 2020. Le sondage évaluait la pertinence des consignes de santé publique pour contenir la COVID-19 dans cette grande communauté. Des données quantitatives et qualitatives ont été saisies et décrites. RéSULTATS: En tout, 106 adultes vivant dans la réserve ont répondu; plus de 80 % étaient des femmes. Les difficultés d'accès aux aliments selon la situation d'emploi étaient significatives (p = 0,0004). Les personnes à faible revenu trouvaient plus difficile d'accéder aux aliments (p = 0,0190) et d'obtenir des soins médicaux essentiels (p = 0,0060), de se procurer des vêtements (p = 0,0280) et de trouver de moyens de transport (p = 0,0027). Certains répondants ont fait état de pertes de revenus associées à leurs expériences de la COVID-19, et de difficultés d'accès aux fournitures essentielles. Les répondants ont trouvé que « bien se laver les mains ¼ était la consigne la plus facile à respecter (98 %), et que « rester à 2 mètres les uns des autres ¼ était la plus difficile (23 %). De nombreux répondants ont trouvé facile de respecter les consignes de santé publique dans leur domaine personnel et d'accorder la priorité à « la santé familiale ¼, mais ont éprouvé des difficultés à négocier les obligations et aspects sociaux, particulièrement lorsqu'ils ne pouvaient pas contrôler les actions des autres. Les gens ont dit vouloir des informations claires, mais ont parfois critiqué la riposte à la COVID-19. CONCLUSION: Les Premiers Peuples font face à de nombreuses difficultés supplémentaires dans le cadre de la riposte à la COVID-19; ces difficultés résultent en partie de problèmes persistants liés à d'importants facteurs sociétaux, économiques et systémiques.

Mental health experiences with COVID-19 public health measures in an Alberta First Nations Community.

BACKGROUND: First Nations (FN) people of Canada experience health, social, and systemic inequities due to colonization. Consequently, COVID-19 has placed further mental health stress on people related to personal finances, employment security and worry over infection, resulting in exacerbated effects of unresolved past medical and physical traumas. This study aims to understand the experiences related to mental health in an Alberta FN community during the early stages of the pandemic. METHODS: In partnership with FN leadership, the study implemented an online cross-sectional survey. Adults from a large FN community in Alberta, Canada, were asked to complete a survey, including two mental health-related screening questionnaires: (1) Generalized Anxiety Disorder-2 item; and (2) Patient Health Questionnaire-2 item. In addition, respondents could provide responses to open-ended questions about their experiences. RESULTS: Among 106 respondents, 95 (89.6%) finished the survey; 18% of adults screened positive for depressive symptoms (score of 3 or greater) and reported difficulty following public health advice for using hand sanitizer, maintaining social distancing, or self-isolating. 21% of adults screened positive for symptoms of anxiety (score of 3 or greater) and reported difficulty maintaining social distance, self-isolating, obtaining food and clothing, or meeting other basic living requirements. CONCLUSIONS: FN communities may be disproportionately affected by COVID-19, and may experience exacerbated symptoms of anxiety, depression and overall poor mental health and well-being. Additional supports and services, including for mental health, should be considered for FN in the context of COVID-19 public health measures. HIGHLIGHTS: The COVID-19 pandemic has brought upon increased stress and accompanying symptoms of anxiety and depression for a First Nations community in Alberta. Studies, such as this one, that characterize the influence of the COVID-19 pandemic on mental health among First Nations people, are urgently needed because of increasing demands on healthcare systems due to the pandemic and potential delays in the care of patients living with pre-existing mental health conditions. There is an opportunity to capitalize on First Nations people's experiences of post-traumatic growth proactively supporting/maintaining their well-being and possibly the development of community-based mental health interventions and supports.

Development and Validation of a Machine Learning Model to Estimate Risk of Adverse Outcomes Within 30 Days of Opioid Dispensation.

Importance: Machine learning approaches can assist opioid stewardship by identifying high-risk opioid prescribing for potential interventions. Objective: To develop a machine learning model for deployment that can estimate the risk of adverse outcomes within 30 days of an opioid dispensation as a potential component of prescription drug monitoring programs using access to real-world data. Design, Setting, and Participants: This prognostic study used population-level administrative health data to construct a machine learning model. This study took place in Alberta, Canada (from January 1, 2018, to December 31, 2019), and included all patients 18 years and older who received at least 1 opioid dispensation from a community pharmacy within the province. Exposures: Each opioid dispensation served as the unit of analysis. Main Outcomes and Measures: Opioid-related adverse outcomes were identified from administrative data sets. An XGBoost model was developed on 2018 data to estimate the risk of hospitalization, an emergency department visit, or mortality within 30 days of an opioid dispensation; validation on 2019 data was done to evaluate model performance. Model discrimination, calibration, and other relevant metrics are reported using daily and weekly predictions on both ranked predictions and predicted probability thresholds using all data from 2019. Results: A total of 853 324 participants represented 6 181 025 opioid dispensations, with 145 016 outcome events reported (2.3%); 46.4% of the participants were men and 53.6% were women, with a mean (SD) age of 49.1 (15.6) years for men and 51.0 (18.0) years for women. Of the outcome events, 77 326 (2.6% pretest probability) occurred within 30 days of a dispensation in the validation set (XGBoost C statistic, 0.82 [95% CI, 0.81-0.82]). The top 0.1 percentile of estimated risk had a positive likelihood ratio (LR) of 28.7, which translated to a posttest probability of 43.1%. In our simulations, the weekly measured predictions had higher positive LRs in both the highest-risk dispensations and percentiles of estimated risk compared with predictions measured daily. Net benefit analysis showed that using machine learning prediction may not add additional benefit over the entire range of probability thresholds. Conclusions and Relevance: These findings suggest that prescription drug monitoring programs can use machine learning classifiers to identify patients at risk of opioid-related adverse outcomes and intervene on high-risk ranked predictions. Better access to available administrative and clinical data could improve the prediction performance of machine learning classifiers and thus expand opioid stewardship efforts.

Safe opioid prescribing: a prognostic machine learning approach to predicting 30-day risk after an opioid dispensation in Alberta, Canada.

OBJECTIVE: To develop machine learning models employing administrative health data that can estimate risk of adverse outcomes within 30 days of an opioid dispensation for use by health departments or prescription monitoring programmes. DESIGN, SETTING AND PARTICIPANTS: This prognostic study was conducted in Alberta, Canada between 2017 and 2018. Participants included all patients 18 years of age and older who received at least one opioid dispensation. Pregnant and cancer patients were excluded. EXPOSURE: Each opioid dispensation served as an exposure. MAIN OUTCOMES/MEASURES: Opioid-related adverse outcomes were identified from linked administrative health data. Machine learning algorithms were trained using 2017 data to predict risk of hospitalisation, emergency department visit and mortality within 30 days of an opioid dispensation. Two validation sets, using 2017 and 2018 data, were used to evaluate model performance. Model discrimination and calibration performance were assessed for all patients and those at higher risk. Machine learning discrimination was compared with current opioid guidelines. RESULTS: Participants in the 2017 training set (n=275 150) and validation set (n=117 829) had similar baseline characteristics. In the 2017 validation set, c-statistics for the XGBoost, logistic regression and neural network classifiers were 0.87, 0.87 and 0.80, respectively. In the 2018 validation set (n=393 023), the corresponding c-statistics were 0.88, 0.88 and 0.82. C-statistics from the Canadian guidelines ranged from 0.54 to 0.69 while the US guidelines ranged from 0.50 to 0.62. The top five percentile of predicted risk for the XGBoost and logistic regression classifiers captured 42% of all events and translated into post-test probabilities of 13.38% and 13.45%, respectively, up from the pretest probability of 1.6%. CONCLUSION: Machine learning classifiers, especially incorporating hospitalisation/physician claims data, have better predictive performance compared with guideline or prescription history only approaches when predicting 30-day risk of adverse outcomes. Prescription monitoring programmes and health departments with access to administrative data can use machine learning classifiers to effectively identify those at higher risk compared with current guideline-based approaches.

Concurrent use of opioids and benzodiazepines/Z-drugs in Alberta, Canada and the risk of hospitalisation and death: a case cross-over study.

OBJECTIVES: Coprescribing of benzodiazepines/Z-drugs (BZDs) and opioids is a drug-use pattern of considerable concern due to risk of adverse events. The objective of this study is to estimate the effect of concurrent use of BZDs on the risk of hospitalisations/emergency department (ED) visits and deaths among opioid users. DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based case cross-over study during 2016-2018 involving Albertans 18 years of age and over who received opioids. From this group, we identified 1 056 773 people who were hospitalised or visited the ED, and 31 998 who died. INTERVENTION: Concurrent use of opioids and BZDs. OUTCOMES: We estimated the risk of incident all-cause hospitalisation/ED visits and all-cause mortality associated with concurrent BZD use by applying a matched-pair analyses comparing concurrent use to opioid only use. RESULTS: Concurrent BZD use occurred in 17% of opioid users (179 805/1 056 773). Overall, concurrent use was associated with higher risk of hospitalisation/ED visit (OR 1.13, p<0.001) and all cause death (OR 1.90; p<0.001). The estimated risk of hospitalisation/ED visit was highest in those >65 (OR 1.5; p<0.001), using multiple health providers (OR 1.67; p<0.001) and >365 days of opioid use (OR 1.76; p<0.001). Events due to opioid toxicity were also associated with concurrent use (OR 1.8; p<0.001). Opioid dose-response effects among concurrent patients who died were also noted (OR 3.13; p<0.001). INTERPRETATION: Concurrent use of opioids and BZDs further contributes to the risk of hospitalisation/ED visits and mortality in Alberta, Canada over opioid use alone, with higher opioid doses, older age and increased number of unique health providers carrying higher risks. Regulatory bodies and health providers should reinforce safe drug-use practices and be vigilant about coprescribing.

Characterisation of concurrent use of prescription opioids and benzodiazepine/Z-drugs in Alberta, Canada: a population-based study.

OBJECTIVE: The objective of this study is to characterise concurrent use of benzodiazepine receptor modulators and opioids among prescription opioid users in Alberta in 2017. DESIGN: A population based retrospective study. SETTING: Alberta, Canada, in the year 2017. PARTICIPANTS: All individuals in Alberta, Canada, with at least one dispensation record from a community pharmacy for an opioid in the year 2017. EXPOSURE: Concurrent use of a benzodiazepine receptor modulator and opioid, defined as overlap of supply for both drugs for at least 1 day. MAIN OUTCOME MEASURES: Prevalence of concurrency was estimated among subgroups of patient characteristics that were considered clinically relevant or associated with inappropriate medication use. RESULTS: Among the 547 709 Albertans who were dispensed opioid prescriptions in 2017, 132 156 (24%) also received prescriptions for benzodiazepine receptor modulators. There were 96 581 (17.6%) prescription opioid users who concurrently used benzodiazepine receptor modulators with an average of 98 days (SD=114, 95% CI 97 to 99) of total cumulative concurrency and a median of 37 days (IQR 10 to 171). The average longest duration of consecutive days of concurrency was 45 (SD=60, 95% CI 44.6 to 45.4) with a median of 24 days (IQR 8 to 59). Concurrency was more prevalent in females, patients using an average daily oral morphine equivalent >90 mg, opioid dependence therapy patients, chronic opioid users, patients utilising a high number of unique providers, lower median household incomes and those older than 65 (p value<0.001 for all comparisons). CONCLUSIONS: Concurrent prescribing of opioids and benzodiazepine receptor modulators is common in Alberta despite the ongoing guidance of many clinical resources. Older patients, those taking higher doses of opioids, and for longer durations may be at particular risk of adverse outcomes and may be worthy of closer follow-up for assessment for dose tapering or discontinuations. As well, those with higher healthcare utilisation (seeking multiple providers) should also be closely monitored. Continued surveillance of concurrent use of these medications is warranted to ensure that safe drug use recommendations are being followed by health providers.

Benzodiazepine receptor agonist dispensations in Alberta: a population-based descriptive study.

BACKGROUND: There is increasing concern over the use of benzodiazepine receptor agonists (BZRAs). The objective of this study was to describe BZRA dispensations in the province of Alberta in 2015 according to age, sex and appropriateness. METHODS: A population-based descriptive study of people 10 years of age or older with at least 1 BZRA dispensation in Alberta, Canada, between Jan. 1 and Dec. 31, 2015, was conducted. Prevalence of BZRA use, characteristics of BZRAs dispensations, use at the individual level and appropriateness were determined. RESULTS: A total of 372 870 people received 2 463 585 BZRA dispensations in Alberta in 2015. Prevalence of use at the population level was 10% overall, increased with age (p value for trend < 0.001) and was consistently highest among females. Twenty percent of patients used both Z-drugs and benzodiazepines. BZRA users had an average of 7 dispensations (standard deviation [SD] 20), 137 days of use overall (SD 123) and a maximum period of consecutive use of 90 days (SD 95). Days of consecutive use were highest among those aged 65 years or older (126 d). A total of 62 795 (17%) people used more than 1 distinct BZRA ingredient concurrently and 10% had 3 or more distinct prescribers. INTERPRETATION: The prevalence of BZRA use was high and a substantial proportion of use appeared to be potentially inappropriate. This study supports the need for continued monitoring for the prescribing and use of these medications at the population level.

The integration of barcode scanning technology into Canadian public health immunization settings.

BACKGROUND: As part of a series of feasibility studies following the development of Canadian vaccine barcode standards, we compared barcode scanning with manual methods for entering vaccine data into electronic client immunization records in public health settings. METHODS: Two software vendors incorporated barcode scanning functionality into their systems so that Algoma Public Health (APH) in Ontario and four First Nations (FN) communities in Alberta could participate in our study. We compared the recording of client immunization data (vaccine name, lot number, expiry date) using barcode scanning of vaccine vials vs. pre-existing methods of entering vaccine information into the systems. We employed time and motion methodology to evaluate time required for data recording, record audits to assess data quality, and qualitative analysis of immunization staff interviews to gauge user perceptions. RESULTS: We conducted both studies between July and November 2012, with 628 (282 barcoded) vials processed for the APH study, and 749 (408 barcoded) vials for the study in FN communities. Barcode scanning led to significantly fewer immunization record errors than using drop-down menus (APH study: 0% vs. 1.7%; p=0.04) or typing in vaccine data (FN study: 0% vs. 5.6%; p<0.001). There was no significant difference in time to enter vaccine data between scanning and using drop-down menus (27.6s vs. 26.3s; p=0.39), but scanning was significantly faster than typing data into the record (30.3s vs. 41.3s; p<0.001). Seventeen immunization nurses were interviewed; all noted improved record accuracy with scanning, but the majority felt that a more sensitive scanner was needed to reduce the occasional failures to read the 2D barcodes on some vaccines. CONCLUSION: Entering vaccine data into immunization records through barcode scanning led to improved data quality, and was generally well received. Further work is needed to improve barcode readability, particularly for unit-dose vials.