Marc Simard

Introduction: Long COVID is a frequent post-infectious chronic condition that impacts quality of life and work performance. Whether individuals with comorbidities experience a greater functional impact of long COVID is unknown. We evaluated the functional impact of long COVID among healthcare workers (HCWs) with chronic cardiovascular diseases, chronic respiratory diseases, obesity or a history of depression, and compared it with that of HCWs without comorbidities. Methods: We conducted a cross-sectional study in Quebec, Canada. We compared self-reported long COVID cases to COVID-19-infected controls without long COVID on work ability, work functioning, health-related absenteeism, dyspnoea-associated impairment and psychological distress among HCWs (a) with at least one of the four comorbidities, (b) with each of the four comorbidities and (c) without comorbidities. We used inverse probability of exposure and robust Poisson regressions to estimate adjusted prevalence differences (aPD) and prevalence ratios. Comorbidity data were obtained from the Quebec integrated chronic disease surveillance system. Results: A total of 3754 and 8439 HCWs with and without comorbidities, respectively, were included. Among HCWs with at least one of the four comorbidities, long COVID was associated with higher prevalence of low work ability (aPD=15%, 95% CI: 12% to 18%), low work functioning (aPD=27%, 95% CI: 22% to 31%), health-related long-term absenteeism (aPD=8%, 95% CI: 5% to 11%), dyspnoea-associated impairment (aPD=23%, 95% CI: 19% to 26%) and psychological distress (aPD=24%, 95% CI: 20% to 28%). aPDs were greater among HCWs with comorbidities than among those without for low work ability (p=0.013 for interaction), for low work functioning (p=0.034) and for dyspnoea-associated impairment (p<0.001). Conclusion: Long COVID is associated with significant functional impairment among HCWs with pre-existing chronic conditions. HCWs with at least one of the four comorbidities experience lower work ability, lower work functioning and more dyspnoea-associated impairment compared with those without comorbidities.

Background: Potentially inappropriate medications (PIMs) are frequent in older adults, contributing to hospitalizations, adverse events, and healthcare burden. We aimed to estimate direct PIM cost trends from 2012 to 2021 among older women and men in Quebec, Canada. Methods: Using medico-administrative data, we assessed direct costs paid by the public insurer (medication cost and professional fee, excluding out-of-pocket payments by individuals) of PIMs claimed by adults ≥65 years covered by the public drug plan. Costs for 16 PIM classes, identified using 2015 and 2019 Beers criteria, were calculated and stratified by sex and age group (65-74, 75-84, ≥85) for each fiscal year. We assessed the proportion of PIMs among all medication expenditures. We computed average costs/enrollee and usage prevalence for the costliest PIM classes. Trends were estimated using univariate linear regression with 95% confidence intervals. Results: We found a non-statistically significant decrease in total PIM claim costs, from $206 million in 2012 to $186 million in 2021 (trend: -2.9[-17.4; 11.6]), representing 5.4% of medication expenditures for adults ≥65 in 2021. The reduction in total costs was more accentuated in women, whose annual costs were higher than those of men in all age groups. Average cost/enrollee decreased from $179 to $119 (trend: -7[-19; 5]), with a drop from $216 to $142 for women and $132 to $92 for men. Costs/enrollee were higher in 75-84 and ≥85 age groups. Costliest PIM classes included proton-pump inhibitors, benzodiazepines, antipsychotics, antidepressants, estrogens (women), and hypoglycemic agents (men). Cost trends did not always follow prevalence trends for these PIM classes. Conclusion: PIM costs among older adults slightly decreased from 2012 to 2021. Appropriate prescribing and deprescribing appear crucial for reducing these costs. Further research should focus on estimating the societal impact and the cost-effectiveness analysis of deprescribing initiatives and other regulatory measures.

Background: As the number of medications increases, the appropriateness of polypharmacy may become questionable due to the heightened risk of medication-related harm. Objectives: (1) To investigate the relationship between the number of current medications used by older adults and three indicators of potentially inappropriate polypharmacy: (a) the mean number of potentially inappropriate medications (PIMs), (b) the average count of drug–drug interactions, and (c) the anticholinergic burden; (2) To characterize the population-based burden of potentially inappropriate polypharmacy by calculating the proportion of individuals with these indicators. Design: We conducted a population-based observational study using the Quebec Integrated Chronic Disease Surveillance System. Methods: We included all individuals over 65 years insured by the public drug plan on April 1st, 2022. For each individual, we calculated the number of current medications and the number of (a) PIMs (Beers 2019), (b) drug–drug interactions (Beers 2019), and (c) anticholinergic burden (Anticholinergic Cognitive Burden (ACB) scale). The association between the number of medications and these indicators was quantified using linear regression. Prevalence with 99% confidence intervals (CIs) was calculated. Results: A total of 1,437,558 individuals (mean age: 75; 55% female) were included, taking an average of 4.9 medications (±4.1). For each additional medication, the mean number of PIMs, drug–drug interactions, and anticholinergic burden increased by 0.11, 0.04, and 0.17, respectively ( p-trend <0.0001). Nearly half the population (45.5%; 99% CI: 45.5–45.5) had a regimen containing ⩾1 PIMs, ⩾1 drug–drug interaction, or an ACB ⩾3. Conclusion: The strong association between the increasing number of medications and reduced polypharmacy quality underscores the importance of medication count beyond therapeutic indications. With widespread medication use, many older adults face quality issues.

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.

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.

Introduction The simplistic definition of polypharmacy, often designated as the concomitant use of five medications or more, does not distinguish appropriate from inappropriate polypharmacy. Classifying polypharmacy according to varying levels of health risk would help optimise medication use. Objective We aimed to characterise different types of polypharmacy among older adults and evaluate their association with mortality and institutionalisation. Methods Using healthcare databases from the Quebec Integrated Chronic Disease Surveillance System, we selected a community-based random sample of the population ≥ 66 years old covered by the public drug plan. Categorical indicators used to describe polypharmacy included number of medications, potentially inappropriate medications (PIMs), drug–drug interactions, enhanced surveillance medications, complex route of administration medications, anticholinergic cognitive burden (ACB) score and use of blister cards. We used a latent class analysis to subdivide participants into distinct groups of polypharmacy. Their association with 3-year mortality and institutionalisation was assessed with adjusted Cox models. Results In total, 93,516 individuals were included. A four-class model was selected with groups described as (1) no polypharmacy (46% of our sample), (2) high-medium number of medications, low risk (33%), (3) medium number of medications, PIM use with or without high ACB score (8%) and (4) hyperpolypharmacy, complex use, high risk (13%). Using the class without polypharmacy as the reference, all polypharmacy classes were associated with 3-year mortality and institutionalisation, with the most complex/inappropriate classes denoting the highest risk (hazard ratio [HR] [95% confidence interval]: class 3, 70-year-old point estimate for mortality 1.52 [1.30–1.78] and institutionalisation 1.86 [1.52–2.29]; class 4, 70-year-old point estimate for mortality 2.74 [2.44–3.08] and institutionalisation 3.11 [2.60–3.70]). Conclusions We distinguished three types of polypharmacy with varying pharmacotherapeutic and clinical appropriateness. Our results highlight the value of looking beyond the number of medications to assess polypharmacy.

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.

Background: We aimed to identify and characterize adult population‐based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification.Methods: We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity‐specific criteria.Results: Twenty‐two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed.Conclusion: There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity‐related confounding in studies.