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.

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.

[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.

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.

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.

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.

Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer.

BACKGROUND: The link between hormones and hair growth is well established. Inconsistent associations have been found between hair patterns and cancer of the prostate, a hormone-dependent organ. We assessed vertex baldness trajectories, chest hair amount, and their relationships with the odds of developing prostate cancer in a large case-control study in Montreal, Canada. METHODS: In-person interviews were conducted with 1,931 incident prostate cancer cases and 1,994 population-based age-matched (±5 years) controls. Participants reported their hair patterns using the validated Hamilton-Norwood scale of baldness for 10-year increments starting at age 30, and their current amount of chest hair. Group-based trajectories were used to identify men sharing similar patterns of vertex baldness severity over adulthood. Multivariable logistic regression assessed associations between indicators of baldness (frontal, vertex, age at onset, severity, and trajectories), chest hair, and odds of prostate cancer. RESULTS: Vertex balding onset at age 30 was associated with increased odds of overall prostate cancer [Odds ratio (OR), 1.30; 95% confidence interval (CI), 1.03-1.64]. Men in the trajectory characterized by early moderate vertex baldness and developing severe baldness had increased odds of overall (OR, 1.42; 95% CI, 1.03-1.96) and especially aggressive prostate cancer (OR, 1.98; 95% CI, 1.21-3.22) compared with men without baldness. Men with little chest hair had higher odds of aggressive tumors than those with a moderate amount/a lot of chest hair. CONCLUSIONS: Early-onset moderate vertex baldness that progresses and having little chest hair may be useful biomarkers of aggressive prostate cancer. IMPACT: Integration of early-onset vertex balding patterns into risk prediction models of aggressive prostate cancer should be envisaged.

Marginal structural models with latent class growth analysis of treatment trajectories: Statins for primary prevention among older adults.

Latent class growth analysis is increasingly proposed as a solution to summarize the observed longitudinal treatment into a few distinct groups. When latent class growth analysis is combined with standard approaches like Cox proportional hazards models, confounding bias is not properly addressed because of time-varying covariates that have a double role of confounders and mediators. We propose to use latent class growth analysis to classify individuals into a few latent classes based on their medication adherence pattern, then choose a working marginal structural model that relates the outcome to these groups. The parameter of interest is defined as a projection of the true marginal structural model onto the chosen working model. Simulation studies are used to illustrate our approach and compare it with unadjusted, baseline covariates adjusted, time-varying covariates adjusted, and inverse probability of trajectory groups weighted adjusted models. Our proposed approach yielded estimators with little or no bias and appropriate coverage of confidence intervals in these simulations. We applied our latent class growth analysis and marginal structural model approach to a database comprising information on 52,790 individuals from the province of Quebec, Canada, aged more than 65 and who were statin initiators to estimate the effect of statin-usage trajectories on a first cardiovascular event.

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.

Estimating causal effects of physical activity and sedentary behaviours on the development of type 2 diabetes in at-risk children from childhood to late adolescence: an analysis of the QUALITY cohort.

BACKGROUND: Uncertainty remains regarding the causal effect of physical activity and sedentary behaviours on the development of type 2 diabetes in children. We aimed to estimate average treatment effects of physical activity and sedentary behaviours on risk of type 2 diabetes in individuals who are at risk during childhood and adolescence. METHODS: We used data from the Quebec Adipose and Lifestyle Investigation in Youth (QUALITY) cohort of children of western European descent (white non-Hispanic race or ethnicity) with a parental history of obesity (defined as a BMI of 30 kg/m2 or more, or a waist circumference of more than 102 cm in men and 88 cm in women) evaluated at the ages of 8-10 years (baseline), 10-12 years (first follow-up cycle), and 15-17 years (second follow-up cycle) in Québec, Canada. We measured moderate-to-vigorous physical activity (MVPA) and sedentary time by accelerometry, and leisure screen time by questionnaire at each cycle. Outcomes included fasting and 2 h post-load glycaemia and validated indices of insulin sensitivity and insulin secretion. We estimated average treatment effects of MVPA, sedentary time, and screen time on markers of type 2 diabetes using longitudinal marginal structural models with time-varying exposures, outcomes, and confounders from the ages of 8-10 to 15-17 years and inverse probability of treatment and censoring weighting. We considered both the current and cumulative effects of exposures on outcomes. FINDINGS: 630 children were evaluated at baseline (age 8-10 years) between July, 2005, and December, 2008, 564 were evaluated at the first follow-up (age 10-12 years) between July, 2007, and March, 2011, and 377 were evaluated at the second follow-up (age 15-17 years) between September, 2012, and May, 2016. Based on cumulative exposure results, estimated average treatment effects for MVPA were 5·6% (95% CI 2·8 to 8·5) on insulin sensitivity and -3·8% (-7·1 to -0·5) on second-phase insulin secretion per 10 min daily increment from the ages of 8-10 years to age 15-17 years. Average treatment effects for sedentary time and reported screen time resulted in reduced insulin sensitivity (-8·2% [-12·3 to -3·9] and -6·4% [-10·1 to -2·5], respectively), increased second-phase insulin secretion (5·9% [1·9 to 10·1] and 7·0% [-0·1 to 14·7], respectively), and higher fasting glycaemia (0·03 mmol/L [0·003 to 0·05] and 0·02 mmol/L [0·01 to 0·03], respectively) per additional daily hour from the ages of 8-10 years to 15-17 years. INTERPRETATION: Using modern causal inference approaches strengthened the evidence of MVPA and sedentary behaviours as key drivers of development of type 2 diabetes in at-risk children and adolescents, and should be considered as key targets for prevention. FUNDING: Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, and Fonds de Recherche du Québec-Santé. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.

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 .

Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review.

Introduction: The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods. Objective: To review the literature of longitudinal studies using latent growth modeling approaches to study T2D. Methods: MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25th, 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies. Results: From the 4,694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Conclusion: Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification.

Does group-based trajectory modeling estimate spurious trajectories?

BACKGROUND: Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptible to generating spurious findings in some circumstances. METHODS: Six plausible scenarios, two of which mimicked published analyses, were simulated. Models with 1 to 10 trajectory subgroups were estimated and the model that minimized the Bayes criterion was selected. For each scenario, we assessed whether the method identified the correct number of trajectories, the correct shapes of the trajectories, and the mean number of participants of each trajectory subgroup. The performance of the average posterior probabilities, relative entropy and mismatch criteria to assess classification adequacy were compared. RESULTS: Among the six scenarios, the correct number of trajectories was identified in two, the correct shapes in four and the mean number of participants of each trajectory subgroup in only one. Relative entropy and mismatch outperformed the average posterior probability in detecting spurious trajectories. CONCLUSION: Researchers should be aware that GBTM can generate spurious findings, especially when the average posterior probability is used as the sole criterion to evaluate model fit. Several model adequacy criteria should be used to assess classification adequacy.

Current status of neuroprotective and neuroregenerative strategies in multiple sclerosis: A systematic review.

BACKGROUND: Immune-mediated demyelination and consequent degeneration of oligodendrocytes and axons are hallmark features of multiple sclerosis (MS). Remyelination declines in progressive MS, causing permanent axonal loss and irreversible disabilities. Strategies aimed at enhancing remyelination are critical to attenuate disease progression. OBJECTIVE: We systematically reviewed recent advances in neuroprotective and regenerative therapies for MS, covering preclinical and clinical studies. METHODS: We searched three biomedical databases using defined keywords. Two authors independently reviewed articles for inclusion based on pre-specified criteria. The data were extracted from each study and assessed for risk of bias. RESULTS: Our search identified 7351 studies from 2014 to 2020, of which 221 met the defined criteria. These studies reported 262 interventions, wherein 92% were evaluated in animal models. These interventions comprised protein, RNA, lipid and cellular biologics, small molecules, inorganic compounds, and dietary and physiological interventions. Small molecules were the most highly represented strategy, followed by antibody therapies and stem cell transplantation. CONCLUSION: While significant strides have been made to develop regenerative treatments for MS, the current evidence illustrates a skewed representation of the types of strategies that advance to clinical trials. Further examination is thus required to address current barriers to implementing experimental treatments in clinical settings.

Cigarette Smoking Trajectories in Adolescent Smokers: Does the Time Axis Metric Matter?

INTRODUCTION: Most studies modeling adolescent cigarette smoking trajectories use age as the time axis, possibly obscuring depiction of the natural course of cigarette smoking. We used a simulated example and real data to contrast smoking trajectories obtained from models that used time since smoking onset or calendar time (age) as the time axis. METHODS: Data were drawn from a longitudinal investigation of 1293 grade 7 students (mean age 12.8 years) recruited from 10 high schools in Montreal, Canada in 1999-2000, who were followed into young adulthood. Cigarette consumption was measured every 3 months during high school, and again at mean ages 20.4 and 24.0. Analyses using time since onset of smoking as the time metric was restricted to 307 incident smokers; analysis using calendar time included 645 prevalent and incident smokers. Smoking status and nicotine dependence (ND) were assessed at mean ages 20.4 and 24.0. Simulated data mimicked the real study during high school. RESULTS: Use of different time metrics resulted in different numbers and shapes of trajectories in the simulated and real datasets. Participants in the calendar time analyses reported more ND in young adulthood, reflecting inclusion of 388 prevalent smokers who had smoked for longer durations. CONCLUSIONS: Choosing the right time metric for trajectory analysis should be balanced against research intent. Trajectory analyses using the time since onset metric depict the natural course of smoking in incident smokers. Those using calendar time offer a snapshot of smoking across ages during a given time period. IMPLICATIONS: This study uses simulated and real data to show that trajectory analyses of cigarette smoking that use calendar time (e.g., age) versus time since onset as the time axis metric tell a different story. Trajectory analyses using the time since onset metric depict the natural course of smoking in incident smokers. Those using calendar time offer a snapshot of smoking across ages during a given time period. Choosing the right time metric should be balanced against research intent.

Low Social Support at Work and Ambulatory Blood Pressure in a Repeated Cross-sectional Study of White-Collar Workers.

OBJECTIVES: Previous studies on the effect of low social support at work on blood pressure showed mixed results. Few previous studies have used ambulatory blood pressure and examined whether the effect of low social support at work vary among men and women. The aim of this study was to examine the association between low social support at work, ambulatory blood pressure means and hypertension prevalence, in a sample of white-collar workers men and women. METHODS: A repeated cross-sectional design was used. Data were collected three times during a 5-year period, among 3919 white-collar women and men. At each time, coworker and supervisor social support at work were measured using validated scales. Ambulatory blood pressure was measured every 15 min during a working day. General estimating equations were used. RESULTS: In adjusted models, women exposed to low coworker (+0.6 mmHg) and low supervisor social support at work (+0.7 mmHg) had slightly higher diastolic blood pressure means when compared to unexposed women. In men, those with low coworker social support at work had higher diastolic (+0.7 mmHg) blood pressure while those with low supervisor social support had a higher prevalence of hypertension (prevalence ratio = 1.14, 95% CI: 1.04-1.24). CONCLUSIONS: Men with low supervisor social support at work had a higher prevalence of hypertension. Low social support at work was associated with modest increases in diastolic blood pressure among men and women. Workplace prevention strategies aiming to increase social support at work could lead to beneficial effects on worker's cardiovascular health.

A non-parametric propensity score for estimating the effect of interferon-beta or glatiramer acetate on long-term outcomes of multiple sclerosis.

BACKGROUND: The few observational studies that investigated the long-term effects of interferon-beta and glatiramer acetate were usually focused on progression to irreversible disability and other outcomes such as number of relapses and transition to secondary-progressive multiple sclerosis (SPMS) have been rarely studied. The objective of this paper is to estimate the effect of interferon-beta/glatiramer acetate on progression to irreversible disability, transition from relapsing-remitting multiple sclerosis (RRMS) to SPMS and the rate of relapses over 10 years. METHODS: Analyses included 2498 patients with confirmed diagnosis of RRMS followed in Montréal from 1977 to 2016. Marginal structural models with propensity score for treatment and censoring were used to account for potential confounding and attrition. Specifically, we used pooled logistic regression for progression to irreversible disability and transition to SPMS, and Poisson models for the rate of relapses. RESULTS: 77% of subjects were female and the median age at RRMS diagnosis was 35 years. The hazard of progression to irreversible disability was lower among treated patients than untreated patients (HR=0.73, 95% CI [0.57-0.94]). We did not find evidence of an association between interferon-beta/glatiramer acetate and the rate of transition to SPMS either over the 3-month intervals or for the duration of treatment. Patients treated for >5 years had a lower rate of relapses compared to those untreated (HR=0.70, 95% CI [0.57-0.86]). CONCLUSION: Treatment with interferon-beta/glatiramer acetate suggests a beneficial effect on progression to irreversible disability and rate of relapses, but not on transition to SPMS.