Traditional methods hold their ground against machine learning in predicting potentially inappropriate medication use in older adults.

OBJECTIVES: Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for conducting interventions aimed at improving medication outcomes. It remains uncertain whether machine learning methods could enhance the identification of potentially inappropriate medication use among older adults compared to traditional methods. The aim of this study was 1) to compare the performances of machine learning models in predicting use of potentially inappropriate medications and 2) to quantify and compare the relative importance of predictors in a population of community dwelling older adults (>65 years) in the province of Quebec, Canada. METHODS: We used the Quebec Integrated Chronic Disease Surveillance System and selected a cohort of 1,105,295 older adults of whom 533,719 were potentially inappropriate medication users. Potentially inappropriate medications were defined according to the Beers list. We compared performances between five popular machine learning models (gradient boosting machines, logistic regression, naïve Bayes, neural networks, and random forests) based on ROC curves and other performance criteria, using a set of sociodemographic and medical predictors. RESULTS: No model clearly outperformed the others. All models except neural networks were in agreement regarding the top predictors (sex and anxiety-depressive disorders and schizophrenia) and the bottom predictors (rurality and social and material deprivation indices). CONCLUSIONS: Including other types of predictors (e.g., unstructured data) may be more useful for increasing performance in prediction of potentially inappropriate medication use.

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.

Polypharmacy and risk of fractures in older adults: A systematic review.

BACKGROUND: Fractures have serious health consequences in older adults. While some medications are individually associated with increased risk of falls and fractures, it is not clear if this holds true for the use of many medications (polypharmacy). We aimed to identify what is known about the association between polypharmacy and the risk of fractures in adults aged ≥65 and to examine the methods used to study this association. METHODS: We conducted a systematic review with narrative synthesis of studies published up to October 2023 in PubMed, Embase, CINAHL, PsychINFO, Cochrane Library, Web of Science, and the grey literature. Two independent reviewers screened titles, abstracts, and full texts, then performed data extraction and quality assessment. RESULTS: Among the 31 studies included, 11 different definitions of polypharmacy were used and were based on three medication counting methods (concurrent use 15/31, cumulative use over a period 6/31, daily average 3/31, and indeterminate 7/31). Overall, polypharmacy was frequent and associated with higher fracture risk. A dose-response relationship between increasing number of medications and increased risk of fractures was observed. However, only seven studies adjusted for major confounders (age, sex, and chronic disease). The quality of the studies ranged from poor to high. CONCLUSIONS: Polypharmacy appears to be a relevant modifiable risk factor for fractures in older individuals that can easily be used to identify those at risk. The diversity of medication calculation methods and definitions of polypharmacy highlights the importance of a detailed methodology to understand and compare results.

Evidence-based data for newly approved medications' use in older adults: An analysis from clinical trials to monographs.

BACKGROUND: Older adults have historically been excluded from clinical trials, limiting evidence-based data. An updated picture of the situation with newly marketed medications is needed. We aimed to describe (1) the recommendations specific to older adults in monographs of newly marketed medications; (2) the representation of older adults in clinical trials of those medications. METHODS: In November 2020, we listed all medications that received a notice of compliance from Health Canada between January 2006 and September 2020, excluding those with indications irrelevant to community-dwelling older adults and locally acting medications. We assessed the availability and clarity of recommendations in monographs regarding older adults. Using Clinicaltrials.gov, we identified randomized controlled double-blind Phase III-IV trials led in Canada/United States of a sample of 30 commonly used medications among those previously listed. We extracted information on study design, participants, and efficacy/safety analysis specific to older patients. We used simple linear regression and Chi-square/Fisher's exact tests to analyze time trends in the representation of older adults over different periods. RESULTS: A total of 195 monographs were included. Of the 130 monographs reporting a dosing recommendation in older adults, 53 (41%) also reported limited/insufficient data in this population or its subgroups. Of the 373 trials included, 217 (58%) did not integrate a maximum age as an inclusion criterion. However, only 113 (30%) reported including a proportion of older adults representative (or over-representative) of the Canadian older population. Most trials (n = 289; 78%) did not provide efficacy or safety data specific to older adults. In our sample, the number/proportion of older adults in trials seemed to be increasingly reported over time, either explicitly or implicitly (e.g., inclusion criterion specifies <65 years old). CONCLUSIONS: Newly marketed medications still appear to under-represent older adults. The resulting lack of clear recommendations in monographs compromises evidence-based practice, thereby perpetuating the risk to older adults' health.

How does deprescribing (not) reduce mortality? A review of a meta-analysis in community-dwelling older adults casts uncertainty over claimed benefits.

Some meta-analyses suggest that deprescribing may reduce mortality. Our aim was to determine the underlying factors contributing to this observed reduction. We analysed data from 12 randomized controlled trials included in the latest meta-analysis on deprescribing in community-dwelling older adults. Our analysis focused on deprescribed medications and potential methodological concerns. Only a third (4/12) of the trials aimed to study mortality, and that too as a secondary outcome. Five trials reported a reduction in total medications, potentially inappropriate medications or drug-related problems. Information on specific classes of deprescribed medications was limited, although a wide array was concerned (e.g., antihypertensive, sedative, gastro-intestinal medications and vitamins). Follow-up periods were ≤1 year in 11 trials, and five trials included ≤150 participants. Small sample sizes often resulted in imbalanced groups (e.g., comorbidities, number of potentially inappropriate medications), yet no trials presented multivariable analyses. In the two trials with the most weight in the meta-analysis, several deaths occurred before the intervention, making it difficult to draw conclusions about the impact of the deprescribing intervention on mortality. These methodological issues cast significant uncertainty on the benefits of deprescribing on mortality outcomes. Large-scale, well-designed trials are needed to address this issue effectively.

Affective States During the First Wave of the COVID-19 Pandemic: Progression of Intensity and Relation With Public Health Compliance Behavior.

The COVID-19 pandemic was expected to cause intense affective reactions. This situation provided a unique opportunity to examine the characteristics and correlates of emotions in a real-world context with great significance. Our study aimed to describe the progression of positive and negative affective states during the pandemic, and to investigate which affective states predicted compliance with public health measures. We undertook a survey of affective states in the province of Quebec at the beginning, the peak, and the aftermath of the first wave of the COVID-19 pandemic. We recruited 530 responders; 154 responded to all three surveys. We used self-report scales to measure affective states and compliance with public health measures. We then computed separate linear regressions for the three phases of our study, with compliance with health measures as the dependent variable. Affective states were generally most intense at the beginning of the pandemic. Fear-related pandemic-related affective states reliably predicted compliance with public health measures in the three phases of our study. Positively valenced affective states related to the societal response also contributed predictive value, but only at the peak of the first wave.

Exploring the mechanisms responsible for the modulating role of frowning in emotional reasoning: An ERP study.

Studies show that emotions impact reasoning, and that emotions are embodied. A recent study revealed that emotions embodied in facial expressions can modulate the impact of emotional content on reasoning accuracy. In the current study, we aimed to explore the mechanisms responsible for the impact of frowning on emotional reasoning using electrophysiology. We examined two reasoning-related ERPs: the N400 related to inference process and the N2 related to conflict detection. We also measured the LPP, associated with sustained attention to emotional stimuli. Twenty-six participants completed a reasoning task with emotional content while we recorded their brain activity with electroencephalography. In one block, they were instructed to solve syllogisms while voluntarily frowning. In another block, they were asked to solve syllogisms while contracting a non-facial muscle. Results revealed that frowning influenced sustained attention towards emotional stimuli, as measured through LPP. Frowning also showed a trend for a deleterious effect on the inference process measured through the N400. In line with the dual process models, this suggests that frowning impacts sustained attention, but surprisingly it might also impact Type 2 processes. This study provides useful insight regarding the link between reasoning and emotions in the body.

Differentiating Real-World Autobiographical Experiences without Recourse to Behaviour.

Investigating human consciousness based on brain activity alone is a key challenge in cognitive neuroscience. One of its central facets, the ability to form autobiographical memories, has been investigated through several fMRI studies that have revealed a pattern of activity across a network of frontal, parietal, and medial temporal lobe regions when participants view personal photographs, as opposed to when they view photographs from someone else's life. Here, our goal was to attempt to decode when participants were re-experiencing an entire event, captured on video from a first-person perspective, relative to a very similar event experienced by someone else. Participants were asked to sit passively in a wheelchair while a researcher pushed them around a local mall. A small wearable camera was mounted on each participant, in order to capture autobiographical videos of the visit from a first-person perspective. One week later, participants were scanned while they passively viewed different categories of videos; some were autobiographical, while others were not. A machine-learning model was able to successfully classify the video categories above chance, both within and across participants, suggesting that there is a shared mechanism differentiating autobiographical experiences from non-autobiographical ones. Moreover, the classifier brain maps revealed that the fronto-parietal network, mid-temporal regions and extrastriate cortex were critical for differentiating between autobiographical and non-autobiographical memories. We argue that this novel paradigm captures the true nature of autobiographical memories, and is well suited to patients (e.g., with brain injuries) who may be unable to respond reliably to traditional experimental stimuli.

Potentially inappropriate medications in older individuals with diabetes: A population-based study in Quebec, Canada.

AIM: To study the population-based prevalence of potentially inappropriate medication (PIM) among older individuals with diabetes, and to identify factors associated with their use. METHODS: We used the Quebec Integrated Chronic Disease Surveillance System (QICDSS) database to conduct a population-based cohort study of individuals with diabetes ≥66 years between April 1st, 2014 and March 31st, 2015. PIMs were defined according to the 2015 Beers Criteria. Factors associated with PIM use were identified using robust Poisson regression models. Risk ratios (RR) and 99% confidence intervals (99%CI) were calculated. RESULTS: More than half (56%) of the 286,962 older individuals with diabetes used at least one PIM over a year. Benzodiazepines (41%), proton pump inhibitors (27%) and endocrine medication (mainly glibenclamide) (25%) were the most common PIMs used. Factors associated with PIM use included female sex (RR: 1.17; 99%CI: 1.16-1.18), and comorbidities such as schizophrenia (1.48; 1.45-1.51), anxiety disorders (1.34; 1.33-1.35) and Alzheimer's disease (1.14; 1.13-1.25). Risks of using PIMs increased both with increasing comorbidities and number of medications. CONCLUSION: PIM use is highly prevalent among older individuals with diabetes. Interventions to promote optimal medication use should particularly target individuals with comorbidities and polypharmacy who are most vulnerable to adverse drug events.

One-year persistence of potentially inappropriate medication use in older adults: A population-based study.

AIMS: To assess the 1-year persistence of potentially inappropriate medication (PIM) use and identify associated factors in community-dwelling older adults in Quebec, Canada. METHODS: A population-based cohort study was conducted using the Quebec Integrated Chronic Disease Surveillance System. Individuals insured by the public drug plan and aged ≥66 years who initiated a PIM between 1 April 2014 and 31 March 2015 were followed-up for 1 year. PIMs were identified using the 2015 Beers criteria. One-year persistence of PIM use was defined as continuous treatment with any PIM, without interruption for more than 60 days between prescriptions refills. Poisson regression models were performed to identify factors associated with 1-year persistence of any PIM. RESULTS: In total, 25.1% of PIM initiators were persistent at 1 year. In non-persistent individuals, the median time to PIM discontinuation was 31 days (interquartile range 21-92). Individuals were more persistent at 1 year with antipsychotics (43.9%), long-duration sulphonylureas (40.2%), antiarrhythmics/immediate-release nifedipine (36.5%) and proton pump inhibitors (36.0%). Factors significantly associated with persistence were an increased age, being a man and having a high number of medications and chronic diseases, especially dementia, diabetes and cardiovascular diseases. CONCLUSIONS: One-quarter of community-dwelling older adults are continuously exposed to PIMs. To optimize medication prescribing in the older population, further interventions are needed to limit the use of PIMs most likely to be continued, especially in individuals most at risk of being persistent and also particularly vulnerable to adverse events.

Potentially inappropriate medications in older adults: a population-based cohort study.

BACKGROUND: Non-optimal medication use among older adults is a public health concern. A concrete picture of potentially inappropriate medication (PIM) use is imperative to ensure optimal medication use. OBJECTIVE: To assess the prevalence of PIMs in community-dwelling older adults and identify associated factors. METHODS: A retrospective population-based cohort study was conducted using the Quebec Integrated Chronic Disease Surveillance System (QICDSS). The QICDSS includes data on drug claims for community-dwelling older adults with chronic diseases or at risk of developing chronic diseases aged ≥65 years who are insured by the public drug insurance plan. Individuals aged ≥66 years who were continuously insured with the public drug plan between 1 April 2014 and 31 March 2016 were included. PIMs were defined using the 2015 Beers criteria. We conducted multivariate robust Poisson regression analyses to explore factors associated with PIM use. RESULTS: A total of 1 105 295 individuals were included. Of these, 48.3% were prescribed at least one PIM. The most prevalent PIMs were benzodiazepines (25.7%), proton-pump inhibitors (21.3%), antipsychotics (5.6%), antidepressants (5.0%) and long-duration sulfonylureas (3.3%). Factors associated with PIM exposure included being a woman [rate ratio (RR): 1.20; 95% confidence interval (CI): 1.20-1.21], increased number of medications and having a high number of chronic diseases, especially mental disorders (RR: 1.50; 95% CI: 1.49-1.51). CONCLUSION: Almost one out of two community-dwelling older adults use a PIM. It is imperative to reduce the use of PIMs, by limiting their prescription and by promoting their deprescribing, which necessitates not only the active involvement of prescribers but also patients.

Polypharmacy and Pharmacological Treatment of Diabetes in Older Individuals: A Population-Based Study in Quebec, Canada.

Our objectives were to describe the use of pharmacological treatments in older adults with diabetes and to identify the factors associated with the use of a combination of hypoglycemic, antihypertensive and lipid-lowering agents. Using the Quebec Integrated Chronic Disease Surveillance System, we conducted a population-based cohort study among individuals aged 66-75 years with diabetes in 2014-2015. We described the number of medications and the classes of medications used and calculated the proportion of individuals using at least one medication from each of these classes: hypoglycemics, antihypertensives and lipid-lowering agents. We identified the factors associated with the use of this combination of treatments by performing robust Poisson regressions. The 146,710 individuals used an average of 12 (SD 7) different medications, mostly cardiovascular (91.3% of users), hormones, including hypoglycemic agents (84.5%), and central nervous system medications (79.8%). The majority of individuals (59%) were exposed to the combination of treatments and the factor most strongly associated was the presence of cardiovascular comorbidities (RR: 1.29; 99% CI: 1.28-1.31). Older individuals with diabetes are exposed to a large number of medications. While the use of the combination of treatments is significant and could translate into cardiovascular benefits at the population level, the potential risk associated with polypharmacy needs to be documented.

Normative data for the Clock Drawing Test for French-Quebec mid- and older aged healthy adults.

OBJECTIVE: The Clock Drawing Test (CDT) is frequently used to screen for cognitive impairment, however, normative data for Rouleau et al.'s scoring system are scarce. The present study aims to provide norms for Rouleau et al.'s scoring system that are tailored to Quebec French-speaking mid- and older aged healthy adults. METHODS: Six researchers from various research centers across the Province of Quebec (Canada) sent anonymous data for 593 (391 women) healthy community-dwelling volunteers (age range: 43-93 years; education range: 5-23 years) who completed the CDT 'drawing on command' version. This command version (setting the clock hands to 11:10, without a pre-drawn circle) was administrated as part of a more extensive neuropsychological assessment, or along with cognitive screening instruments. Each drawn clock was scored according to the quantitative criteria set by Rouleau et al.'s scoring system. RESULTS: CDT scores were significantly correlated with age (r(592) = -.132, p = .001) and years of education (r(592) = .116, p = .005), but not with sex (r(592) = .065, p = .112). Since data were skewed towards higher test scores, the percentiles method was used for analysis. Percentile ranks stratified by age and education are presented. CONCLUSION: These normative data for Rouleau et al.'s scoring system will contribute towards adequately screening for cognitive decline in Quebec French-speaking healthy adults, by also taking into account individual characteristics such as age and education.

Cognitive Load Mediates the Effect of Emotion on Analytical Thinking.

Although the detrimental effect of emotion on reasoning has been evidenced many times, the cognitive mechanism underlying this effect remains unclear. In the present paper, we explore the cognitive load hypothesis as a potential explanation. In an experiment, participants solved syllogistic reasoning problems with either neutral or emotional contents. Participants were also presented with a secondary task, for which the difficult version requires the mobilization of cognitive resources to be correctly solved. Participants performed overall worse and took longer on emotional problems than on neutral problems. Performance on the secondary task, in the difficult version, was poorer when participants were reasoning about emotional, compared to neutral contents, consistent with the idea that processing emotion requires more cognitive resources. Taken together, the findings afford evidence that the deleterious effect of emotion on reasoning is mediated by cognitive load.