An Alternative Perspective on the Robust Poisson Method for Estimating Risk or Prevalence Ratios.

The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Poisson method yields results that can be interpreted as risk or prevalence ratios. In addition, it does not suffer from frequent nonconvergence problems such as the most common implementations of maximum likelihood estimators of the log-binomial model. However, using a Poisson distribution to model a binary outcome may seem counterintuitive. Methodologic papers have often presented this as a good approximation to the more natural binomial distribution. In this article, we provide an alternative perspective to the robust Poisson method based on the semiparametric theory. This perspective highlights that the robust Poisson method does not require assuming a Poisson distribution for the outcome. In fact, the method only assumes a log-linear relation between the risk or prevalence of the outcome and the explanatory variables. This assumption and the consequences of its violation are discussed. We also provide suggestions to reduce the risk of violating the modeling assumption. Additionally, we discuss and contrast the robust Poisson method with other approaches for estimating exposure risk or prevalence ratios. See video abstract at, http://links.lww.com/EDE/B987 .

Association between sustained opioid prescription and frequent emergency department use: a cohort study.

BACKGROUND: Chronic non-cancer pain (CNCP) is common among frequent emergency department (ED) users, although factors underlying this association are unclear. This study estimated the association between sustained opioid use and frequent ED use among patients with CNCP. METHODS: Retrospective cohort study using a Canadian provincial health insurer database (Régie d'Assurance Maladie du Québec). The database included adults with both ≥1 chronic condition and ≥ 1 ED visit in 2012 or 2013. Inclusion in the study further required a CNCP diagnosis, public drug insurance coverage and 1-year survival after the first ED visit in 2012 or 2013 (index visit). Multivariable logistic regression was used to derive ORs of frequent ED use (≥5 visits in the year following the index visit) subsequent to sustained opioid use (≥60 days opioids prescription within 90 days preceding the index visit), adjusting for important covariables. RESULTS: From 576 688 patients in the database, 58 237 were included in the study. Of these, 4109 (7.1%) had received a sustained opioid prescription and 4735 (8.1%) were frequent ED users in the follow-up year. Sustained opioid use was not associated with frequent ED use in the multivariable model (OR: 1.06, 95% CI 0.94 to 1.19). Novel associated covariables were benzodiazepine prescription (OR: 1.21, 95% CI 1.12 to 1.30) and polypharmacy (OR: 1.23, 95% CI 1.13 to 1.34). CONCLUSIONS: Due to confounding by social and medical vulnerability, patients with CNCP with sustained opioid use appear to have a higher propensity for frequent ED use in unadjusted models. However, sustained opioid use was not associated with frequent ED use in these patients after adjustment.

CONECT-6: a case-finding tool to identify patients with complex health needs.

BACKGROUND: Early identification of patients with chronic conditions and complex health needs in emergency departments (ED) would enable the provision of services better suited to their needs, such as case management. A case-finding tool would ultimately support ED teams to this end and could reduce the cost of services due to avoidable ED visits and hospitalizations. The aim of this study was to develop and validate a short self-administered case-finding tool in EDs to identify patients with chronic conditions and complex health needs in an adult population. METHODS: This prospective development and initial validation study of a case-finding tool was conducted in four EDs in the province of Quebec (Canada). Adult patients with chronic conditions were approached at their third or more visit to the ED within 12 months to complete a self-administered questionnaire, which included socio-demographics, a comorbidity index, the reference standard INTERMED self-assessment, and 12 questions to develop the case-finding tool. Significant variables in bivariate analysis were included in a multivariate logistic regression analysis and a backward elimination procedure was applied. A receiver operating characteristic (ROC) curve was developed to identify the most appropriate threshold score to identify patients with complex health needs. RESULTS: Two hundred ninety patients participated in the study. The multivariate analysis yielded a six-question tool, COmplex NEeds Case-finding Tool - 6 (CONECT-6), which evaluates the following variables: low perceived health; limitations due to pain; unmet needs; high self-perceived complexity; low income; and poor social support. With a threshold of two or more positive answers, the sensitivity was 90% and specificity 66%. The positive and negative predictive values were 49 and 75% respectively. CONCLUSIONS: The case-finding process is the essential characteristic of case management effectiveness. This study presents the first case-finding tool to identify adult patients with chronic conditions and complex health needs in ED.

Exploring polypharmacy with artificial intelligence: data analysis protocol.

BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. METHODS: We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications. DISCUSSION: The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data.

Risk of Frequent Emergency Department Use Among an Ambulatory Care Sensitive Condition Population: A Population-based Cohort Study.

BACKGROUND: A small fraction of patients use a disproportionately large amount of emergency department (ED) resources. Identifying these patients, especially those with ambulatory care sensitive conditions (ACSC), would allow health care professionals to enhance their outpatient care. OBJECTIVE: The objectives of the study were to determine predictive factors associated with frequent ED use in a Quebec adult population with ACSCs and to compare several models predicting the risk of becoming an ED frequent user following an ED visit. RESEARCH DESIGN: This was an observational population-based cohort study extracted from Quebec's administrative data. SUBJECTS: The cohort included 451,775 adult patients, living in nonremote areas, with an ED visit between January 2012 and December 2013 (index visit), and previously diagnosed with an ACSC but not dementia. MEASURES: The outcome was frequent ED use (≥4 visits) during the year following the index visit. Predictors included sociodemographics, physical and mental comorbidities, and prior use of health services. We developed several logistic models (with different sets of predictors) on a derivation cohort (2012 cohort) and tested them on a validation cohort (2013 cohort). RESULTS: Frequent ED users represented 5% of the cohort and accounted for 36% of all ED visits. A simple 2-variable prediction model incorporating history of hospitalization and number of previous ED use accurately predicted future frequent ED use. The full model with all sets of predictors performed only slightly better than the simple model (area under the receiver-operating characteristic curve: 0.786 vs. 0.759, respectively; similar positive predictive value and number needed to evaluate curves). CONCLUSIONS: The ability to identify frequent ED users based only on previous ED and hospitalization use provides an opportunity to rapidly target this population for appropriate interventions.

Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories.

BACKGROUND: Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts. Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients' characteristics for each CT type. METHODS: This is an observational cohort study extracted from Quebec's medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a 'tailored' multidimensional State Sequence Analysis, based on the "6W" model of Care Trajectories. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why"), and the speciality of care providers ("which"). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients' characteristics using the usual descriptive statistics. RESULTS: The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization. CONCLUSIONS: The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

Care trajectories of individuals with anxiety disorders: A retrospective cohort study.

BACKGROUND: Anxiety disorders (ADs) are associated with increased healthcare use (HCU), and individuals may seek healthcare through various pathways according to clinical and individual characteristics. This study aimed to characterize care trajectories (CTs) of individuals with ADs. METHODS: This is a retrospective cohort study using the Care Trajectories - Enriched Data cohort, a linkage between the Canadian Community Health Surveys (CCHS), and health administrative data from Quebec. The cohort included 5143 respondents reporting ADs to the CCHS between 2009 and 2016. We measured CTs over 5 years before CCHS using a state sequence analysis. RESULTS: The cohort was categorized into five types of CTs. Type 1 (52.7 %) was the lowest care-seeking group, with fewer comorbidities. Type 2 (24.0 %) had higher levels of physical and mental health comorbidities and moderate HCU, mainly ambulatory visits to general practitioners. Type 3 (13.1 %) represented older patients with the highest level of physical illnesses and high HCU, predominantly ambulatory consultation of specialists other than psychiatrists. Types 4 and 5 combined young and middle-aged patients suffering from severe psychological distress. HCU of type 4 (6.7 %) was high, mainly consultations of ambulatory psychiatrists, and HCU of type 5 (3.5 %), was the highest and mostly in acute care. LIMITATIONS: Administrative and survey data may have coding errors, missing data and self-report biases. CONCLUSION: Five types of CTs showed distinct patterns of HCU often modulated by physical and mental health comorbidities, which emphasizes the importance of considering ADs when individuals seek care for other mental health conditions or physical illness.

10-Year Multimorbidity Trajectories in Older People Have Limited Benefit in Predicting Short-Term Health Outcomes in Comparison to Standard Multimorbidity Thresholds: A Population-Based Study.

Purpose: To identify multimorbidity trajectories among older adults and to compare their health outcome predictive performance with that of cross-sectional multimorbidity thresholds (eg, ≥2 chronic conditions (CCs)). Patients and Methods: We performed a population-based longitudinal study with a random sample of 99,411 individuals aged >65 years on April 1, 2019. Using health administrative data, we calculated for each individual the yearly CCs number from 2010 to 2019 and constructed the trajectories with latent class growth analysis. We used logistic regression to determine the increase in predictive capacity (c-statistic) of multimorbidity trajectories and traditional cross-sectional indicators (≥2, ≥3, or ≥4 CCs, assessed in April 2019) over that of a baseline model (including age, sex, and deprivation). We predicted 1-year mortality, hospitalization, polypharmacy, and frequent general practitioner, specialist, or emergency department visits. Results: We identified eight multimorbidity trajectories, each representing between 3% and 25% of the population. These trajectories exhibited trends of increasing, stable, or decreasing number of CCs. When predicting mortality, the 95% CI for the increase in the c-statistic for multimorbidity trajectories [0.032-0.044] overlapped with that of the ≥3 indicator [0.037-0.050]. Similar results were observed when predicting other health outcomes and with other cross-sectional indicators. Conclusion: Multimorbidity trajectories displayed comparable health outcome predictive capacity to those of traditional cross-sectional multimorbidity indicators. Given its ease of calculation, continued use of traditional multimorbidity thresholds remains relevant for population-based multimorbidity surveillance and clinical practice.

A comparative analysis of medication counting methods to assess polypharmacy in medico-administrative databases.

BACKGROUND: The variety of methods for counting medications may lead to confusion when attempting to compare the extent of polypharmacy across different populations. OBJECTIVE: To compare the prevalence estimates of polypharmacy derived from medico-administrative databases, using different methods for counting medications. METHODS: Data were drawn from the Québec Integrated Chronic Disease Surveillance System. A random sample of 110,000 individuals aged >65 was selected, including only those who were alive and covered by the public drug plan during the one-year follow-up. We used six methods to count medications: #1-cumulative one-year count, #2-average of four quarters' cumulative counts, #3-count on a single day, #4-count of medications used in first and fourth quarters, #5-count weighted by duration of exposure, and #6-count of uninterrupted medication use. Polypharmacy was defined as ≥5 medications. Cohen's Kappa was calculated to assess the level of agreement between the methods. RESULTS: A total of 93,516 (85 %) individuals were included. The prevalence of polypharmacy varied across methods. The highest prevalence was observed with cumulative methods (#1:74.1 %; #2:61.4 %). Single day count (#3:47.6 %), first and fourth quarters count (#4:49.5 %), and weighted count (#5:46.6 %) yielded similar results. The uninterrupted use count yielded the lowest estimate (#6:35.4 %). The weighted method (#5) showed strong agreement with the first and fourth quarters count (#4). Cumulative methods identified higher proportions of younger, less multimorbid individuals compared to other methods. CONCLUSION: Counting methods significantly affect polypharmacy prevalence estimates, necessitating their consideration when comparing and interpretating results.

Machine learning to improve frequent emergency department use prediction: a retrospective cohort study.

Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users have complex needs and associated factors are commonly evaluated using logistic regression. However, other machine learning models, especially those exploiting the potential of large databases, have been less explored. This study aims at comparing the performance of logistic regression to four machine learning models for predicting frequent emergency department use in an adult population with chronic diseases, in the province of Quebec (Canada). This is a retrospective population-based study using medical and administrative databases from the Régie de l'assurance maladie du Québec. Two definitions were used for frequent emergency department use (outcome to predict): having at least three and five visits during a year period. Independent variables included sociodemographic characteristics, healthcare service use, and chronic diseases. We compared the performance of logistic regression with gradient boosting machine, naïve Bayes, neural networks, and random forests (binary and continuous outcome) using Area under the ROC curve, sensibility, specificity, positive predictive value, and negative predictive value. Out of 451,775 ED users, 43,151 (9.5%) and 13,676 (3.0%) were frequent users with at least three and five visits per year, respectively. Random forests with a binary outcome had the lowest performances (ROC curve: 53.8 [95% confidence interval 53.5-54.0] and 51.4 [95% confidence interval 51.1-51.8] for frequent users 3 and 5, respectively) while the other models had superior and overall similar performance. The most important variable in prediction was the number of emergency department visits in the previous year. No model outperformed the others. Innovations in algorithms may slightly refine current predictions, but access to other variables may be more helpful in the case of frequent emergency department use prediction.

Exploring the associations between polypharmacy and COVID-19-related hospitalisations and deaths: a population-based cohort study among older adults in Quebec, Canada.

OBJECTIVES: To study the association between polypharmacy and the risk of hospitalisation and death in cases of COVID-19 in the population over the age of 65. DESIGN: Population-based cohort study. SETTING: Quebec Integrated Chronic Disease Surveillance System, composed of five medico-administrative databases, in the province of Quebec, Canada. PARTICIPANTS: 32 476 COVID-19 cases aged over 65 whose diagnosis was made between 23 February 2020 and 15 March 2021, and who were covered by the public drug insurance plan (thus excluding those living in long-term care). We counted the number of different medications they claimed between 1 April 2019 and 31 March 2020. OUTCOME MEASURES: Robust Poisson regression was used to calculate relative risk of hospitalisation and death associated with the use of multiple medications, adjusting for age, sex, chronic conditions, material and social deprivation and living environment. RESULTS: Of the 32 476 COVID-19 cases included, 10 350 (32%) were hospitalised and 4146 (13%) died. Compared with 0-4 medications, polypharmacy exposure was associated with increased hospitalisations, with relative risks ranging from 1.11 (95% CI 1.04 to 1.19) for those using 5-9 medications to 1.62 (95% CI 1.51 to 1.75) for those using 20+. Similarly, the risk of death increased with the number of medications, from 1.13 (95% CI 0.99 to 1.30) for those using (5-9 medications to 1.97 (95% CI 1.70 to 2.27) (20+). Increased risk was mainly observed in younger groups. CONCLUSIONS: Polypharmacy was significantly associated with the risk of hospitalisations and deaths related to COVID-19 in this cohort of older adults. Polypharmacy may represent a marker of vulnerability, especially for younger groups of older adults.

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review.

Introduction: The ongoing collection of large medical data has created conditions for application of artificial intelligence (AI) in research. This scoping review aimed to identify major areas of interest of AI applied to health care administrative data. Methods: The search was performed in seven databases: Medline, Embase, CINAHL, Web of science, IEEE, ICM digital library, and Compendex. We included articles published between January 2001 and March 2021, that described research with AI applied to medical diagnostics, pharmacotherapy, and health outcomes data. We screened the full text content and used natural language processing to automatically extract health areas of interest, principal AI methods, and names of medications. Results: Out of 14,864 articles, 343 were included. We determined ten areas of interest, the most common being health diagnostic or treatment outcome prediction (32%); representation of medical data, clinical pathways, and data temporality (i.e., transformation of raw medical data into compact and analysis-friendly format) (22%); and adverse drug effects, drug-drug interactions, and medication cascades (15%). Less attention has been devoted to areas such as health effects of polypharmacy (1%); and reinforcement learning (1%). The most common AI methods were decision trees, cluster analysis, random forests, and support vector machines. Most frequently mentioned medications included insulin, metformin, vitamins, acetaminophen, and heparin. Conclusions: The scoping review revealed the potential of AI application to health-related studies. However, several areas of interest in pharmacoepidemiology are sparsely reported, and the lack of details in studies related to pharmacotherapy suggests that AI could be used more optimally in pharmacoepidemiologic research.

Profiles of frequent emergency department users with chronic conditions: a latent class analysis.

OBJECTIVES: Frequent emergency department users represent a small proportion of users while cumulating many visits. Previously identified factors of frequent use include high physical comorbidity, mental health disorders, poor socioeconomic status and substance abuse. However, frequent users do not necessarily exhibit all these characteristics and they constitute a heterogeneous population. This study aims to establish profiles of frequent emergency department users in an adult population with chronic conditions. DESIGN: This is a retrospective cohort study using administrative databases. SETTING: All adults who visited the emergency department between 2012 and 2013 (index date) in the province of Quebec (Canada), diagnosed with at least one chronic condition, and without dementia were included. Patients living in remote areas and who died in the year following their index date were excluded. We used latent class analysis, a probability-based model to establish profiles of frequent emergency department users. Frequent use was defined as having five visits or more during 1 year. Patient characteristics included sociodemographic characteristics, physical and mental comorbidities and prior healthcare utilisation. RESULTS: Out of 4 51 775 patients who visited emergency departments at least once in 2012-2013, 13 676 (3.03%) were frequent users. Four groups were identified: (1) 'low morbidity' (n=5501, 40.2%), (2) 'high physical comorbidity' (n=3202, 23.4%), (3) 'injury or chronic non-cancer pain' (n=2313, 19.5%) and (4) 'mental health or alcohol/substance abuse' (n=2660, 16.9%). CONCLUSIONS: The four profiles have distinct medical and socioeconomic characteristics. These profiles provide useful information for developing tailored interventions that would address the specific needs of each type of frequent emergency department users.

Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach.

Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave). Specific analyses are needed, using appropriate statistical tools. Quantile regression can provide such analysis by focusing not only on the conditional median, but on different conditional quantiles of the dependent variable. In particular, high quantiles of a health issue can be treated as health peaks. In this study, quantile regression is used to model the relationships between conditional quantiles of cardiovascular variables and meteorological variables in Montreal (Canada), focusing on health peaks. Results show that meteorological impacts are not constant throughout the conditional quantiles. They are stronger in health peaks compared to quantiles around the median. Results also show that temperature is the main significant variable. This study highlights the fact that classical statistical methods are not appropriate when health peaks are of interest. Quantile regression allows for more precise estimations for health peaks, which could lead to refined public health warnings.

Profiles of Frequent Geriatric Users of Emergency Departments: A Latent Class Analysis.

BACKGROUND/OBJECTIVES: Frequent geriatric users of emergency departments (EDs) represent a complex and heterogeneous population. Identifying their specific subgroups would allow the development of interventions better customized to their needs and characteristics. Thus, this study aimed to develop profiles of frequent geriatric ED users using the individual characteristics of patients. DESIGN: This was a retrospective cohort study. SETTING: Databases from the Régie de l'assurance maladie du Québec (RAMQ) were utilized. PARTICIPANTSThis study included individuals aged 65 years or older living in the community in the Province of Quebec (Canada), who consulted in an ED at least four times in the year after an ED index date (an ED visit, chosen randomly, during an index period of January 1, 2012 to December 31, 2013) and who had received a diagnosis of ambulatory care-sensitive conditions (ACSCs) in the 2 years preceding the index date. MEASUREMENTS: A latent class analysis was used to identify subgroups of frequent geriatric ED users according to their individual characteristics, including ACSC type, dementia, mental health disorders, cancer diagnosis, and comorbidity index. RESULTS: The study cohort consisted of 21,393 frequent geriatric ED users. Four groups of frequent geriatric ED users were identified: people with low comorbidity (39.0%), comprising the individuals with the lowest number of physical and mental health conditions; people with cancer (32.7%); people with pulmonaryand cardiac diseases (18.1%); and people with dementia or mental health disorders (10.2%), composed of individuals with the highest proportion of common and severe mental health disease, as well as dementia. This group accounts for the highest use of overall healthcare services. CONCLUSION: These profiles will be useful in developing customized interventions addressing the needs of each subgroup of frequent geriatric ED users. More research is needed to bridge the remaining gaps, especially regarding the healthiest frequent geriatric users of EDs.

Frequent emergency department use by older adults with ambulatory care sensitive conditions: A population-based cohort study.

AIM: To identify factors associated with frequent emergency department (ED) use among older adults with ambulatory care sensitive conditions. METHODS: This was a retrospective cohort study using databases from the Régie de l'assurance maladie du Québec. We included community-dwelling individuals aged ≥65 years in the Province of Quebec (Canada), who consulted in ED at least once between 2012 and 2013 (index period), and were diagnosed with at least one ambulatory care sensitive condition in the 2 years preceding and including the index date (n = 264 473). We used a multivariate logistic regression model to evaluate the association between independent variables and being a frequent geriatric ED user, defined as four or more visits during the year after the index date. RESULTS: Out of the total study population, 17 332 (6.6%) individuals were considered frequent ED users in the year after the index date, accounting for 38% of ED uses for this period. The main variables associated with frequent geriatric ED use were older age, presence of chronic obstructive pulmonary disorder or diabetes, higher comorbidity index, common mental health disorders, polypharmacy, higher number of past ED and specialist visits, rural residence, and higher material and social deprivation. Dementia was inversely associated with frequent ED use. CONCLUSIONS: Frequent geriatric ED users constitute a complex population whose characteristics need to be managed thoroughly in order to enhance the quality and efficiency of their care. Further studies should address their description in administrative databases so as to combine self-perceived and professionally evaluated variables. Geriatr Gerontol Int 2020; 20: 317-323.

Persistent frequent emergency department users with chronic conditions: A population-based cohort study.

BACKGROUND: Frequent emergency department users are patients cumulating at least four visits per year. Few studies have focused on persistent frequent users, who maintain their frequent user status for multiple consecutive years. This study targets an adult population with chronic conditions, and its aims are: 1) to estimate the prevalence of persistent frequent ED use; 2) to identify factors associated with persistent frequent ED use (frequent use for three consecutive years) and compare their importance with those associated with occasional frequent ED use (frequent use during the year following the index date); and 3) to compare characteristics of "persistent frequent users" to "occasional frequent users" and to "users other than persistent frequent users". METHODS: This is a retrospective cohort study using Quebec administrative databases. All adult patients who visited the emergency department in 2012, diagnosed with chronic conditions, and living in non-remote areas were included. Patients who died in the three years following their index date were excluded. The main outcome was persistent frequent use (≥4 visits per year during three consecutive years). Potential predictors included sociodemographic characteristics, physical and mental comorbidities, and prior healthcare utilization. Odds ratios were computed using multivariable logistic regression. RESULTS: Out of 297,182 patients who visited ED at least once in 2012, 3,357 (1.10%) were persistent frequent users. Their main characteristics included poor socioeconomic status, mental and physical comorbidity, and substance abuse. Those characteristics were also present for occasional frequent users, although with higher percentages for the persistent user group. The number of previous visits to the emergency department was the most important factor in the regression model. The occasional frequent users' attrition rate was higher between the first and second year of follow-up than between the second and third year. CONCLUSIONS: Persistent frequent users are a subpopulation of frequent users with whom they share characteristics, such as physical and mental comorbidities, though the former are poorer and younger. More research is needed in order to better understand what factors can contribute to persistent frequent use.

Statistical tools used for analyses of frequent users of emergency department: a scoping review.

OBJECTIVE: Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user. METHODS: We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis. RESULTS: We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used. CONCLUSIONS: This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.