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.

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.

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”. 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. 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. 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.

Hospitalizations and deaths belong to the most studied health variables in public health. Those variables are usually analyzed through mean events and trends, based on the whole dataset. However, this approach is not appropriate to comprehend health outcome peaks which are unusual events that strongly impact the health care network (e.g. overflow in hospital emergency rooms). Peaks can also be of interest in etiological research, for instance when analyzing relationships with extreme exposures (meteorological conditions, air pollution, social stress, etc.). Therefore, this paper aims at modeling health variables exclusively through the peaks, which is rarely done except over short periods. Establishing a rigorous and general methodology to identify peaks is another goal of this study. To this end, the extreme value theory appears adequate with statistical tools for selecting and modeling peaks. Selection and analysis for deaths and hospitalizations peaks using extreme value theory have not been applied in public health yet. Therefore, this study also has an exploratory goal. A declustering procedure is applied to the raw data in order to meet extreme value theory requirements. The application is done on hospitalization and death peaks for cardiovascular diseases, in the Montreal and Quebec metropolitan communities (Canada) for the period 1981–2011. The peak return levels are obtained from the modeling and can be useful in hospital management or planning future capacity needs for health care facilities, for example. This paper focuses on one class of diseases in two cities, but the methodology can be applied to any other health peaks series anywhere, as it is data driven.

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.

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.