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.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

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.

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.

Trends in Brain Research: A Bibliometric Analysis.

BACKGROUND: Bibliometrics methods have allowed researchers to assess the popularity of brain research through the ever-growing number of brain-related research papers. While many topics of brain research have been covered by previous studies, there is no comprehensive overview of the evolution of brain research and its various specialties and funding practices over a long period of time. OBJECTIVE: This paper aims to (1) determine how brain research has evolved over time in terms of number of papers, (2) countries' relative and absolute positioning in terms of papers and impact, and (3) how those various trends vary by area. METHODS: Using a list of validated keywords, we extracted brain-related articles and journals indexed in the Web of Science over the 1991-2020 period, for a total of 2,467,708 papers. We used three indicators to perform: number of papers, specialization, and research impact. RESULTS: Our results show that over the past 30 years, the number of brain-related papers has grown at a faster pace than science in general, with China being at the forefront of this growth. Different patterns of specialization among countries and funders were also underlined. Finally, the NIH, the European Commission, the National Natural Science Foundation of China, the UK Medical Research Council, and the German Research Foundation were found to be among the top funders. CONCLUSION: Despite data-related limitations, our findings provide a large-scope snapshot of the evolution of brain research and its funding, which may be used as a baseline for future studies on these topics.

Sex-specific medication trajectories in older adults newly diagnosed with diabetes.

Background: People with diabetes tend to use many medications to treat diabetes and comorbidities. Nevertheless, the evolution of polypharmacy in newly diagnosed males and females has been little studied. Objective: The objective of this paper was to identify and describe medication trajectories in incident diabetes cases according to sex. Methods: Data were obtained from the Quebec Integrated Chronic Disease Surveillance System. We built a population-based cohort of community-dwelling individuals aged >65 years diagnosed with diabetes in 2014 who were alive and covered with the public drug plan until March 31, 2019. Latent class models were used to identify medication trajectory groups in males and females separately. Results: Of the 10,363 included individuals, 51.4% were males. Females were older and more likely to have more medication claims than males. Four trajectory groups were identified for males and five for females. Most trajectories showed sustained and stable number of medications over time. For each sex, only one of the trajectory groups included a mean annual number of medications lesser than five. Slight increasing trends of medication use were detected in the trajectories composed of very high users, which included older, more comorbid individuals frequently exposed to potentially inappropriate medications. Conclusions: Most males and females with incident diabetes had a high burden of medication following the year of diagnosis and were classified in a group of sustained medication use over time. The largest increase in medication was among those who had higher level of polypharmacy of questionable quality at baseline, raising concerns about the innocuity of such medication trajectories.

A Bootstrap Approach for Evaluating Uncertainty in the Number of Groups Identified by Latent Class Growth Models.

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.

Impact of chronic comorbidities on hospitalization, intensive care unit admission and death among adult vaccinated and unvaccinated COVID-19 confirmed cases during the Omicron wave.

Background: Comorbidities are important risk factors of severe COVID-19 complications. Their impact during the Omicron wave among vaccinated and unvaccinated COVID-19 cases is not well documented. Purpose: The objective of this study was to estimate the association between the number of comorbidities and the risk of hospitalization, intensive care unit (ICU) admission, and death among vaccinated and unvaccinated confirmed adult COVID-19 cases during the Omicron wave. Research Design and Study sample: We performed a cohort study of COVID-19 adult cases of primo-infection occurring during the Omicron wave, from December 5, 2021 to January 9, 2022 using surveillance database of the province of Québec, Canada. The database included all laboratory-confirmed cases in the province and the related information on 21 pre-existing comorbidities, hospitalization, ICU admission, death related to COVID-19 and vaccination status. Analysis: We performed a robust Poisson regression model to estimate the impact of the number of comorbidities on each complication by vaccination status adjusted for age, sex, socioeconomic status, and living environment. Results: We observed that the risk of complication increased for each additional comorbidity in both vaccinated and unvaccinated individuals and that this risk was systematically higher among unvaccinated individuals. Compared with vaccinated individuals without comorbidities (reference group), the risks of hospitalization, ICU admission, and death were respectively: 9X (95% CI [7.77-12.01]), 13X (95% CI [8.74-18.87]), and 12X (95% CI [7.57-18.91]) higher in vaccinated individuals with ≥3 comorbidities; 22X (95% CI [19.07-25.95]), 45X (95% CI [29.06-69.67]) and 38X (95% CI [23.62-61.14]) higher in unvaccinated individuals with ≥3 comorbidities. Conclusion: Our results support the importance of promoting vaccination in all individuals, and especially those with pre-existing medical conditions, to reduce severe complications, even during the Omicron wave.

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 .

Chronic diseases and variations in rates of antimicrobial use in the community: a population-based analysis of linked administrative data in Quebec, Canada, 2002-2017.

BACKGROUND: Chronic diseases may increase risk of infection and complications from infections; fear of these risks may lower clinicians' tolerance threshold for the prescription of antimicrobials, thus increasing the risk of selecting resistant bacteria. We sought to describe rates of antimicrobial use in Quebec and measure the association between chronic diseases and utilization rates. METHODS: Using the Quebec Integrated Chronic Disease Surveillance System, we analyzed data of people covered by the public drug insurance plan in 2002-2017. Based on delivered prescriptions, we described trends in antimicrobial use in the population, and per category of select chronic diseases (i.e., none, respiratory, cardiovascular, diabetes, mental disorder), according to age group (0-17 yr, 18-64 yr and ≥ 65 yr). We computed ratios of extended-to-narrow-spectrum antimicrobials in 2014-2017. We used robust Poisson regression to quantify the association between chronic diseases and rates of antimicrobial use among children and adults (≥ 18 yr). RESULTS: Between 2002 and 2017, 4 231 724 prescriptions were received over 6 653 473 individual-years among children; 1 367 492 (20.6%) individual-years had at least 1 chronic disease. Among adults aged 18-64 years, 13 365 577 prescriptions were received over 24 935 592 individual-years; 9 533 493 (38.2%) individual-years had at least 1 chronic disease. Among adults 65 years or older, 11 689 365 prescriptions were received over 15 927 342 individual-years; 12 743 588 (80.0%) individual-years had least 1 chronic disease. Antimicrobial use decreased among children, remained stable among younger adults and increased among older adults. Trends were consistent across chronic disease categories in children and older adults. In 2014-2017, 19.9% of children, 39.1% of younger adults and 79.7% of older adults had at least 1 chronic disease. Claims for extended-spectrum antimicrobials were frequent in all age and chronic disease groups, relative to narrow-spectrum antimicrobials (ratios from 3.1:1 to 14.6:1). Antimicrobial use was higher among people with respiratory diseases (adults: relative rate [RR] 2.09, 95% confidence interval [CI] 2.07-2.10; children: RR 1.62, 95% CI 1.59-1.65), mental health diagnoses (adults: RR 1.48, 95% CI 1.46-1.49; children: RR 1.22, 95% 1.20-1.24), diabetes (adults: RR 1.40, 95% CI 1.28-1.41; children: RR 2.02, 95% CI 1.58-2.57) and cardiovascular diseases (adults: RR 1.31, 95% CI 1.30-1.32), compared with those with none of the studied chronic diseases. INTERPRETATION: During the study period, large proportions of antimicrobial prescriptions were for people with chronic diseases, across the age spectrum. Interventions to reduce antimicrobial use should be tailored for these populations.

Chronic diseases and compliance with provincial guidelines for outpatient antibiotic prescription in cases of otitis media and respiratory infections: a population-based study of linked data in Quebec, Canada, 2010-2017.

BACKGROUND: In Quebec, antibiotic use is higher among outpatients with chronic diseases. We sought to measure compliance with provincial guidelines for the treatment of otitis media and common respiratory infections, and to measure variations in compliance according to the presence of certain chronic diseases. METHODS: We conducted a population-based study of linked data on antibiotic dispensing covered by the public drug insurance plan between April 2010 and March 2017. We included patients who had consulted a primary care physician within 2 days before being dispensed an antibiotic for an infection targeted by provincial guidelines, including bronchitis in patients with chronic obstructive pulmonary disease, otitis media, pharyngitis, pneumonia and sinusitis. We computed proportions of prescriptions compliant with guidelines (use of recommended antibiotic for children, and use of recommended antibiotic and dosage for adults) by age group (children or adults) and chronic disease (respiratory, cardiovascular, diabetes, mental disorder or none). We measured the impact of chronic diseases on compliance using robust Poisson regression. RESULTS: We analyzed between 14 677 and 198 902 prescriptions for each infection under study. Compliance was greater than 87% among children, but was lower among children with asthma (proportion ratios between 0.97 and 1.00). In adults, the chosen antibiotic was compliant for at least 73% of prescriptions, except for pharyngitis (≤ 61%). Accounting for dosage lowered compliance to between 31% and 61%. Compliance was lower in the presence of chronic diseases (proportion ratios between 0.94 and 0.98). INTERPRETATION: It is possible that prescribing noncompliant prescriptions was sometimes appropriate, but the high frequency of noncompliance suggests room for improvement. Given that variations associated with chronic diseases were small, disease-specific guidelines for antibiotic prescriptions are likely to have a limited impact on compliance.

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.

National differences in dissemination and use of open access literature.

Open Access (OA) dissemination has been gaining a lot of momentum over the last decade, thanks to the implementation of several OA policies by funders and institutions, as well as the development of several new platforms that facilitate the publication of OA content at low or no cost. Studies have shown that nearly half of the contemporary scientific literature could be available online for free. However, few studies have compared the use of OA literature across countries. This study aims to provide a global picture of OA adoption by countries, using two indicators: publications in OA and references made to articles in OA. We find that, on average, low-income countries are publishing and citing OA at the highest rate, while upper middle-income countries and higher-income countries publish and cite OA articles at below world-average rates. These results highlight national differences in OA uptake and suggest that more OA initiatives at the institutional, national, and international levels are needed to support wider adoption of open scholarship.

Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy.

Future global Visible Shortwave Infrared Imaging Spectrometers, such as the Surface Biology and Geology (SBG) mission, will regularly cover the Earth's entire terrestrial land area. These missions need high fidelity atmospheric correction to produce consistent maps of terrestrial and aquatic ecosystem traits. However, estimation of surface reflectance and atmospheric state is computationally challenging, and the terabyte data volumes of global missions will exceed available processing capacity. This article describes how missions can overcome this bottleneck using the spatial continuity of atmospheric fields. Contemporary imaging spectrometers oversample atmospheric spatial variability, so it is not necessary to invert every pixel. Spatially sparse solutions can train local linear emulators that provide fast, exact inversions in their vicinity. We find that estimating the atmosphere at 200 m scales can outperform traditional atmospheric correction, improving speed by one to two orders of magnitude with no measurable penalty to accuracy. We validate performance with an airborne field campaign, showing reflectance accuracies with RMSE of 1.1% or better compared to ground measurements of diverse targets. These errors are statistically consistent with retrieval uncertainty budgets. Local emulators can close the efficiency gap and make rigorous model inversion algorithms feasible for global missions such as SBG.

Polypharmacy among older individuals with heart failure: trends between 2000 and 2017 in the province of Quebec, Canada.

OBJECTIVE: Pharmacological management of heart failure and comorbidities may result in polypharmacy, but there are few population-based studies that portray the use of medications over time. We aimed to describe the trends in polypharmacy and medication use in older adults with heart failure. METHODS: We performed a study including all adults >65 years with heart failure between 2000 and 2017 using health administrative databases in Quebec, Canada. Medication use was ascertained by the presence of at least one claim in each year. We defined three levels of polypharmacy: ⩾10, ⩾15 and ⩾20 different medications/year, and evaluated the use of guideline-recommended and potentially inappropriate medications. We calculated age- and sex-standardized proportions of users each year. RESULTS: The use of ⩾10, ⩾15 and ⩾20 medications increased from 62.2%, 30.6% and 12.2% in 2000 to 71.9%, 43.9% and 22.7%, respectively, in 2017. The combination of ß-blocker and angiotensin-converting enzyme inhibitor (ACEI)/angiotensin II receptor blocker (ARB) was used by 30.4% of individuals in 2000 and 45.5% in 2017. ACEI/ARB users decreased from 65.8% in 2000 to 62.1% in 2017. Potentially inappropriate medication use decreased over time. CONCLUSION: Polypharmacy is significant among older adults with heart failure. Implications of such medication burden should be investigated.

Changes in Polypharmacy and Psychotropic Medication Use After Diagnosis of Major Neurocognitive Disorders: A Population-based Study in Québec, Canada.

BACKGROUND: Older adults with major neurocognitive disorder (MNCD) are often exposed to polypharmacy. We aimed to assess the prescribing and discontinuation patterns of medications following diagnosis of MNCD among community-dwelling older adults. METHODS: Using the Quebec Integrated Chronic Disease Surveillance System, we conducted a population-based cohort study comparing 1-year prediagnosis and postdiagnosis use of medications between a group of individuals older than 65 years newly diagnosed with MNCD in 2016-2017 and a control group without MNCD. The difference-in-difference method was used to estimate the prediagnosis and postdiagnosis variation in the number of medications prescribed and in the proportion of psychotropic and anticholinergic medication users. RESULTS: In the MNCD group, the mean number of medications used (excluding Alzheimer disease treatments) increased by 1.25 in the year after the diagnosis. The respective increase was 0.45 in the control group, yielding an adjusted difference-in-differences of 0.81 (95% confidence interval: 0.74; 0.87) between groups. The adjusted difference-in-differences in the proportions of antipsychotic, antidepressant, and anticholinergic medication users was 13.2% (12.5; 13.9), 7.1% (6.5; 7.7), and 3.8% (3.1; 4.6), respectively. CONCLUSIONS: The medication burden among older adults tends to increase in the year following a diagnosis of MNCD. The use of antipsychotics and antidepressants may explain a part of the observed increase.

Comparison of Socioeconomic Disparities in Pump Uptake Among Children With Type 1 Diabetes in 2 Canadian Provinces With Different Payment Models.

Importance: Insulin pumps improve glycemic control and quality of life in children with type 1 diabetes (T1D). Canada's provinces have implemented universal pediatric programs to improve access. However, these programs provide differing financial coverage, allowing for unique cross-jurisdictional comparisons. Objective: To evaluate possible socioeconomic status (SES) disparities in pump uptake in Québec, where pumps are fully funded, with those in Manitoba, where pumps are partially funded. Design, Setting, and Participants: Using health administrative databases and a clinical registry, parallel, population-based cohort studies of children with diabetes were conducted from April 1, 2011, in Québec, and April 1, 2012, in Manitoba, until March 31, 2017. In analysis conducted from July 1, 2019, to November 30, 2021, multivariable Cox proportional hazards regression models were applied to study the association between pump uptake and SES, defined using validated area-based material and social deprivation indices. Children aged 1 to 17 years with T1D were identified using a validated definition in administrative data (Québec) and a clinical registry (Manitoba). Those using pumps before the initiation of provincial programs were excluded. Exposures: Socioeconomic status. Main Outcomes and Measures: Insulin pump uptake. Results: A total of 2919 children with T1D were identified in Québec: 1550 male (53.1%), mean (SD) age at diagnosis, 8.3 (4.4) years, and 1067 (36.6%) were using a pump. In Manitoba, 636 children were identified: 364 male (57.2%), mean (SD) age at diagnosis, 8.8 (4.4) years, and 106 (16.7%) were using a pump. In Québec, the mean age at diagnosis of T1D was lower in children using the pump compared with those not using a pump (7.6 [4.1] vs 8.7 [4.5] years); sex distribution was similar (562 [52.7%] vs 988 [53.3%] male). No differences in mean (SD) age at diagnosis (8.8 [4.4] vs 8.8 [4.3] years) or sex (57 [53.8%] vs 307 [57.9%] male) were noted in both groups in Manitoba. Increasing material deprivation was associated with decreased pump uptake in both Québec (adjusted hazard ratio [aHR] 0.89; 95% CI, 0.85-0.93) and Manitoba (aHR, 0.70; 95% CI, 0.60-0.82). Inclusion of ethnic concentration did not change this association. Socioeconomic disparities in pump uptake were greater in Manitoba than Québec (P = .006 by t test; Cochran Q, 8.15; P = .004; I2 = 87.7%; 95% CI, 52.5%-96.8%). Conclusions and Relevance: The results of this study suggest that the program of full coverage for pumps available in Québec partially mitigates observed SES disparities in uptake and may be a model to improve access for all children with T1D.

Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic.

BACKGROUND: As we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada. METHODS: We used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 m or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance. RESULTS: A total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9-7.3) before the pandemic to 3.2 (95%CI 2.5-3.9) during the first wave among individuals with comorbidities and from 8.1 (95%CI 7.3-9.0) to 2.7 (95%CI 2.2-3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5-3.2) vs 3.9 (95%CI 3.5-4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4-4.7) and without comorbidities (4.5, 95%CI 4.1-4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities. CONCLUSIONS: It will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).

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.