Potential Biases in Test-Negative Design Studies of COVID-19 Vaccine Effectiveness Arising from the Inclusion of Asymptomatic Individuals.

The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A "classical" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms. In this article, we use directed acyclic graphs and simulations to investigate potential biases in TND studies of COVID-19 VE arising from the use of this "alternative" approach, particularly when applied during periods of widespread testing. We show that the inclusion of asymptomatic individuals can potentially lead to collider stratification bias, uncontrolled confounding by health and healthcare-seeking behaviors (HSBs), and differential outcome misclassification. While our focus is on the COVID-19 setting, the issues discussed here may also be relevant in the context of other infectious diseases. This may be particularly true in scenarios where there is either a high baseline prevalence of infection, a strong correlation between HSBs and vaccination, different testing practices for vaccinated and unvaccinated individuals, or settings where both the vaccine under study attenuates symptoms of infection and diagnostic accuracy is modified by the presence of symptoms.

Psychosocial Stressors at Work and Atrial Fibrillation Incidence: An 18-Year Prospective Study.

BACKGROUND: Psychosocial stressors at work, defined by the job strain and effort-reward imbalance at work (ERI) models, were shown to increase coronary heart disease risk. No previous study has examined the adverse effect of psychosocial stressors at work from both models on atrial fibrillation (AF) incidence. The objective of this study was to examine the separate and combined effect of psychosocial stressors at work from the job strain and ERI models on AF incidence in a prospective cohort study. METHODS AND RESULTS: A total of 5926 white-collar workers (3021 women and 2905 men) free of cardiovascular disease at baseline were followed for an average of 18 years. Job strain (high psychological demands combined with low decision latitude) and ERI were assessed using validated instruments. AF events were identified in medical databases with universal coverage. Hazard ratios (HRs) with 95% CIs were estimated using Cox regression models, controlling for socioeconomic characteristics and lifestyle-related and clinical risk factors. A total of 186 AF incident events were identified over 18 years. Workers exposed to job strain (HR, 1.83 [95% CI, 1.14-2.92]) and ERI (HR, 1.44 [95% CI, 1.05-1.98]) had a higher risk of AF in fully adjusted models. Combined exposure to job strain and ERI was associated with a 2-fold AF risk increase (HR, 1.97 [95% CI, 1.26-3.07]). CONCLUSIONS: Psychosocial stressors at work from the job strain and ERI models are associated with an increased risk of AF, separately and in combination. Workplace prevention strategies targeting these psychosocial stressors at work may be effective to reduce the burden associated with AF.

History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome.

In previous work, we introduced a framework that combines latent class growth analysis (LCGA) with marginal structural models (LCGA-MSM). LCGA-MSM first summarizes the numerous time-varying treatment patterns into a few trajectory groups and then allows for a population-level causal interpretation of the group differences. However, the LCGA-MSM framework is not suitable when the outcome is time-dependent. In this study, we propose combining a nonparametric history-restricted marginal structural model (HRMSM) with LCGA. HRMSMs can be seen as an application of standard MSMs on multiple time intervals. To the best of our knowledge, we also present the first application of HRMSMs with a time-to-event outcome. It was previously noted that HRMSMs could pose interpretation problems in survival analysis when either targeting a hazard ratio or a survival curve. We propose a causal parameter that bypasses these interpretation challenges. We consider three different estimators of the parameters: inverse probability of treatment weighting (IPTW), g-computation, and a pooled longitudinal targeted maximum likelihood estimator (pooled LTMLE). We conduct simulation studies to measure the performance of the proposed LCGA-HRMSM. For all scenarios, we obtain unbiased estimates when using either g-computation or pooled LTMLE. IPTW produced estimates with slightly larger bias in some scenarios. Overall, all approaches have good coverage of the 95 % confidence interval. We applied our approach to a population of older Quebecers composed of 57,211 statin initiators and found that a greater adherence to statins was associated with a lower combined risk of cardiovascular disease or all-cause mortality.

Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding.

Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross-sectional case but have not yet been evaluated and compared in longitudinal studies with time-varying treatment. We have first extended the definition of several balance metrics to the case of a time-varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best. Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population-wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time-points.

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 with traditional methods. This study aimed to (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 Québec, Canada. METHODS: We used the Québec 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 5 popular machine learning models (gradient boosting machines, logistic regression, naive Bayes, neural networks, and random forests) based on receiver operating characteristic 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 (eg, unstructured data) may be more useful for increasing performance in prediction of potentially inappropriate medication use.

Impact of Educational Activity Formats, Online or In-Person, on the Intention of Medical Specialists to Adopt a Clinical Behaviour: A Comparative Study.

COVID-19 accelerated continuing professional development (CPD) delivered online. We aimed to compare the impact of in-person versus online CPD courses on medical specialists' behavioural intentions and subsequent behaviour. In this comparative before-and-after study, medical specialists attended in-person courses on nine clinical topics. A second group attended an adapted online version of these courses. Behavioural intention and its psychosocial determinants were measured before and immediately after the courses. Behaviour change was measured six months later. Generalised estimating equation (GEE) models were used to compare the impact of course formats. A total of 82/206 in-person registrants (mean age: 52±10 years; 50% men) and 318/506 on-line registrants (mean age: 49±12 years; men: 63%) participated. Mean intention before in-person courses was 5.99±1.31 and 6.43±0.80 afterwards (average intention gain 0.44, CI: 0.16-0.74; p=0.003); mean intention before online courses was 5.53±1.62 and 5.98±1.40 afterwards (average intention gain of 0.45, CI: 0.30-0.58; p<0.0001). Difference in intention gain between groups was not statistically significant. Behaviour reported six months later was not significantly associated with post-course intention in either group. However, the intention difference increased significantly among those who said they had adopted the targeted behaviour (paired wilcoxon test: n = 40 and p-value=0.002) while it did not increase significantly in the group of those who had not adopted a targeted behaviour (paired wilcoxon test: n = 16 and p-value=0.223). In conclusion, the increase in intention of specialists after CPD courses was similar whether the course was in-person or online. Also, an increase in intention in both groups signalled more likelihood of adoption.

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.

Strategies aiming to improve statin therapy adherence in older adults: a systematic review.

BACKGROUND: Randomized clinical trials have shown that, under optimal conditions, statins reduce the risk of cardiovascular events in older adults. Given the prevalence and consequences of suboptimal adherence to statin among older adults, it is essential to document strategies designed to increase statin adherence in this population. The objective of this systematic review is to describe and summarize the effectiveness of interventions to improve statin adherence in older adults (≥ 65 years old). METHODS: This review followed PRISMA guidelines. Studies were identified from PubMed, PsycINFO, Embase, CINAHL and Web of Science. Study selection was conducted independently by four reviewers working in pairs. Included studies reported data on interventions designed to increase adherence to statin therapy in older adults and were original trials or observational studies. Interventions were pragmatically regrouped into 8 different categories going from patient to administrative level. Two reviewers extracted study data and assessed study quality independently. Given the heterogeneity between the included studies, a narrative critique and summary was conducted. RESULTS: Twelve out of the 2889 identified articles were included in the review. Our review showed that simplifying patients' drug regimen, administrative improvements and large-scale pharmacy-led automated telephone interventions show positive effects on patient adherence to statin therapy, with odds ratios between > 1.0 and 3.0, while education-based strategies and intensified patient care showed mixed results. CONCLUSIONS: Current evidence suggests that some interventions can increase statin adherence in older adults, which could help in the reduction of the risk of a cardiovascular event in this population.

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.

Value-based comparison of ambulatory children with respiratory diseases in an emergency department and a walk-in clinic: a retrospective cohort study in Québec, Canada.

OBJECTIVE: To compare health outcomes and costs given in the emergency department (ED) and walk-in clinics for ambulatory children presenting with acute respiratory diseases. DESIGN: A retrospective cohort study. SETTING: This study was conducted from April 2016 to March 2017 in one ED and one walk-in clinic. The ED is a paediatric tertiary care centre, and the clinic has access to lab tests and X-rays. PARTICIPANTS: Inclusion criteria were children: (1) aged from 2 to 17 years old and (2) discharged home with a diagnosis of upper respiratory tract infection (URTI), pneumonia or acute asthma. MAIN OUTCOME MEASURES: The primary outcome measure was the proportion of patients returning to any ED or clinic within 3 and 7 days of the index visit. The secondary outcome measures were the mean cost of care estimated using time-driven activity-based costing and the incidence of antibiotic prescription for URTI patients. RESULTS: We included 532 children seen in the ED and 201 seen in the walk-in clinic. The incidence of return visits at 3 and 7 days was 20.7% and 27.3% in the ED vs 6.5% and 11.4% in the clinic (adjusted relative risk at 3 days (aRR) (95% CI) 3.17 (1.77 to 5.66) and aRR at 7 days 2.24 (1.46 to 3.44)). The mean cost (95% CI) of care (CAD) at the index visit was $C96.68 (92.62 to 100.74) in the ED vs $C48.82 (45.47 to 52.16) in the clinic (mean difference (95% CI): 46.15 (41.29 to 51.02)). Antibiotic prescription for URTI was less common in the ED than in the clinic (1.5% vs 16.4%; aRR 0.10 (95% CI 0.03 to 0.32)). CONCLUSIONS: The incidence of return visits and cost of care were significantly higher in the ED, while antibiotic use for URTI was more frequent in the walk-in clinic. These data may help determine which setting offers the highest value to ambulatory children with acute respiratory conditions.

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.

[Effects of extended aromatase inhibitors in women with hormone-dependent breast cancer who have already received five years of adjuvant hormone therapy: A systematic review and meta-analysis]. / Analyse des effets des inhibiteurs de l'aromatase en traitement prolongé chez les femmes ménopausées atteintes d'un cancer du sein non métastatique hormonodépendant ayant déjà reçu cinq ans d'hormonothérapie adjuvante : revue systématique et méta-analyse.

INTRODUCTION: Evaluating the benefits and risks of prolonged hormonal treatment with aromatase inhibitors (AIs) for treating hormone-dependent breast cancer. METHODS: A systematic review and meta-analysis was conducted. Studies reporting on randomized clinical trials concerning prolongating hormonal therapy with AIs as compared to a placebo or no prolongation, after an initial five years of hormonal therapy, were eligible. RESULTS: Seven clinical trials were included. Prolonged AI therapy was associated with a statistically significant improvement in disease-free survival (RR=0.70, 95% CI 0.60 to 0.80). A statistically significant increase was observed for osteoporosis (RR=1.17, 95% CI 1.03 to 1.33), hot flushes/flashes (RR=1.27, 95% CI 1.08 to 1.49), myalgia (RR=1.23, 95% CI 1.09 to 1.39), fractures (RR=1.26, 95% CI 1.09 to 1.45) and arthralgia (RR=1.17, 95% CI 1.10 to 1.25). However, no statistically significant association was observed between prolonged AI therapy and overall survival, cardiovascular events, and bone pain. DISCUSSION: Prolonged AI therapy has significant benefits in terms of disease-free survival in women with hormone-dependent breast cancer. However, adverse effects and a lack of evidence for a benefit on overall survival must be considered in the decision-making process regarding adjuvant hormone therapy extension.

Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

Effect of statin use for the primary prevention of cardiovascular disease among older adults: a cautionary tale concerning target trials emulation.

OBJECTIVES: Evidence concerning the effect of statins in primary prevention of cardiovascular disease (CVD) among older adults is lacking. Using Quebec population-wide administrative data, we emulated a hypothetical randomized trial including older adults >65 years on April 1, 2013, with no CVD history and no statin use in the previous year. STUDY DESIGN AND SETTING: We included individuals who initiated statins and classified them as exposed if they were using statin at least 3 months after initiation and nonexposed otherwise. We followed them until March 31, 2018. The primary outcome was the composite endpoint of coronary events (myocardial infarction, coronary bypass, and percutaneous coronary intervention), stroke, and all-cause mortality. The intention-to-treat (ITT) effect was estimated with adjusted Cox models and per-protocol effect with inverse probability of censoring weighting. RESULTS: A total of 65,096 individuals were included (mean age = 71.0 ± 5.5, female = 55.0%) and 93.7% were exposed. Whereas we observed a reduction in the composite outcome (ITT-hazard ratio (HR) = 0.75; 95% CI: 0.68-0.83) and mortality (ITT-HR = 0.69; 95% CI: 0.61-0.77) among exposed, coronary events increased (ITT-HR = 1.46; 95% CI: 1.09-1.94). All multibias E-values were low indicating that the results were not robust to unmeasured confounding, selection, and misclassification biases simultaneously. CONCLUSION: We cannot conclude on the effectiveness of statins in primary prevention of CVD among older adults. We caution that an in-depth reflection on sources of biases and careful interpretation of results are always required in observational studies.

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.

Single-dose Effectiveness of Mpox Vaccine in Quebec, Canada: Test-negative Design With and Without Adjustment for Self-reported Exposure Risk.

INTRODUCTION: During the 2022 mpox outbreak, the province of Quebec, Canada, prioritized first doses for pre-exposure vaccination of people at high mpox risk, delaying second doses due to limited supply. We estimated single-dose mpox vaccine effectiveness (VE) adjusting for virus exposure risk based only on surrogate indicators available within administrative databases (eg, clinical record of sexually transmitted infections) or supplemented by self-reported risk factor information (eg, sexual contacts). METHODS: We conducted a test-negative case-control study between 19 June and 24 September 2022. Information from administrative databases was supplemented by questionnaire collection of self-reported risk factors specific to the 3-week period before testing. Two study populations were assessed: all within the administrative databases (All-Admin) and the subset completing the questionnaire (Sub-Quest). Logistic regression models adjusted for age, calendar-time and exposure-risk, the latter based on administrative indicators only (All-Admin and Sub-Quest) or with questionnaire supplementation (Sub-Quest). RESULTS: There were 532 All-Admin participants, of which 199 (37%) belonged to Sub-Quest. With exposure-risk adjustment based only on administrative indicators, single-dose VE estimates were similar among All-Admin and Sub-Quest populations at 35% (95% confidence interval [CI]:-2 to 59) and 30% (95% CI:-38 to 64), respectively. With adjustment supplemented by questionnaire information, the Sub-Quest VE estimate increased to 65% (95% CI:1-87), with overlapping confidence intervals. CONCLUSIONS: Using only administrative data, we estimate one vaccine dose reduced the mpox risk by about one-third; whereas, additionally adjusting for self-reported risk factor information revealed greater vaccine benefit, with one dose instead estimated to reduce the mpox risk by about two-thirds. Inadequate exposure-risk adjustment may substantially under-estimate mpox VE.

Test negative design for vaccine effectiveness estimation in the context of the COVID-19 pandemic: A systematic methodology review.

BACKGROUND: During the height of the global COVID-19 pandemic, the test-negative design (TND) was extensively used in many countries to evaluate COVID-19 vaccine effectiveness (VE). Typically, the TND involves the recruitment of care-seeking individuals who meet a common clinical case definition. All participants are then tested for an infection of interest. OBJECTIVES: To review and describe the variation in TND methodology, and disclosure of potential biases, as applied to the evaluation of COVID-19 VE during the early vaccination phase of the pandemic. METHODS: We conducted a systematic review by searching four biomedical databases using defined keywords to identify peer-reviewed articles published between January 1, 2020, and January 25, 2022. We included only original articles that employed a TND to estimate VE of COVID-19 vaccines in which cases and controls were evaluated based on SARS-CoV-2 laboratory test results. RESULTS: We identified 96 studies, 35 of which met the defined criteria. Most studies were from North America (16 studies) and targeted the general population (28 studies). Outcome case definitions were based primarily on COVID-19-like symptoms; however, several papers did not consider or specify symptoms. Cases and controls had the same inclusion criteria in only half of the studies. Most studies relied upon administrative or hospital databases assembled for a different (non-evaluation) clinical purpose. Potential unmeasured confounding (20 studies), misclassification of current SARS-CoV-2 infection (16 studies) and selection bias (10 studies) were disclosed as limitations by some studies. CONCLUSION: We observed potentially meaningful deviations from the validated design in the application of the TND during the COVID-19 pandemic.

SARS-CoV-2 infection among healthcare workers: the role of occupational and household exposures during the first three pandemic waves in Quebec, Canada.

Objective: We described the evolution of SARS-CoV-2 source of infection in a cohort of healthcare workers (HCWs) of Quebec, Canada, during the first three pandemic waves. We also estimated their household secondary attack rate (SAR) and its risk factors. Design: Cross-sectional surveys. Participants: HCWs with a SARS-CoV-2 infection confirmed by polymerasa chain reaction and diagnosed between March 2020 and May 2021. Methods: We collected demographic, clinical, vaccination, and employment information, self-reported perceived source of infection, and transmission to household members during the first three pandemic waves. SAR was calculated for households with ≥2 members where the HCW was the index case. A Poisson regression model estimated the association between risk factors and SAR. Results: Among the 11,670 HCWs completing the survey, 91%, perceived their workplace as the source of infection during the first wave (March-July 2020), 71% during the second wave (July 2020-March 2021), and 40% during the third wave (March-May 2021). Conversely, HCWs reported an increasing proportion of household-acquired infections with each wave from 4% to 14% and 33%, respectively. The overall household SAR of 7,990 HCWs living with ≥1 person was 30% (95%CI: 29-30). SAR increased with the presence of symptoms, older age, and during Alpha-variant predominant period. Conclusions: HCWs and their household members were largely affected during the first pandemic waves of COVID-19, but the relative importance of occupational exposure changed overtime. Pandemic preparedness in healthcare settings is essential to protect HCWs from emerging biological hazard exposures.

Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology.

INTRODUCTION: Plasmode simulations are a type of simulations that use real data to determine the synthetic data-generating equations. Such simulations thus allow evaluating statistical methods under realistic conditions. As far as we know, no plasmode algorithm has been proposed for simulating longitudinal data. In this paper, we propose a longitudinal plasmode framework to generate realistic data with both a time-varying exposure and time-varying covariates. This work was motivated by the objective of comparing different methods for estimating the causal effect of a cumulative exposure to psychosocial stressors at work over time. METHODS: We developed two longitudinal plasmode algorithms: a parametric and a nonparametric algorithms. Data from the PROspective Québec (PROQ) Study on Work and Health were used as an input to generate data with the proposed plasmode algorithms. We evaluated the performance of multiple estimators of the parameters of marginal structural models (MSMs): inverse probability of treatment weighting, g-computation and targeted maximum likelihood estimation. These estimators were also compared to standard regression approaches with either adjustment for baseline covariates only or with adjustment for both baseline and time-varying covariates. RESULTS: Standard regression methods were susceptible to yield biased estimates with confidence intervals having coverage probability lower than their nominal level. The bias was much lower and coverage of confidence intervals was much closer to the nominal level when considering MSMs. Among MSM estimators, g-computation overall produced the best results relative to bias, root mean squared error and coverage of confidence intervals. No method produced unbiased estimates with adequate coverage for all parameters in the more realistic nonparametric plasmode simulation. CONCLUSION: The proposed longitudinal plasmode algorithms can be important methodological tools for evaluating and comparing analytical methods in realistic simulation scenarios. To facilitate the use of these algorithms, we provide R functions on GitHub. We also recommend using MSMs when estimating the effect of cumulative exposure to psychosocial stressors at work.