Marie-Pierre Sylvestre

Immediate and delayed impact of oral glucocorticoid therapy on risk of serious infection in older patients with rheumatoid arthritis: a nested case–control analysis

Objectives To explore the relationship of serious infection risk with current and prior oral glucocorticoid (GC) therapy in elderly patients with rheumatoid arthritis (RA). Methods A case-control analysis matched 1947 serious infection cases to five controls, selected from 16207 RA patients aged ≥65 between 1985–2003 in Quebec, Canada. Adjusted odds ratios for infection associated with different GC patterns were estimated using conventional models and a weighted cumulative dose (WCD) model. Results The WCD model predicted risks better than conventional models. Current and recent GC doses had highest impact on current risk. Doses taken up to 2.5 years ago were also associated with increased risk, albeit to a lesser extent. A current user of 5mg prednisolone had a 30%, 46% or 100% increased risk of serious infection when used continuously for the last 3 months, 6 months or 3 years, respectively, compared to a non-user. The risk associated with 5mg prednisolone taken for the last 3 years was similar to that associated with 30mg taken for the last month. Discontinuing a two-year course of 10mg prednisolone six months ago halved the risk compared to ongoing use. Conclusions GC therapy is associated with infection risk in older patients with RA. The WCD model provided more accurate risk estimates than conventional models. Current and recent doses have greatest impact on infection risk, but the cumulative impact of doses taken in the last 2–3 years still affects risk. Knowing how risk depends on pattern of GC use will contribute to an improved benefit/harm assessment.

Immediate and past cumulative effects of oral glucocorticoids on the risk of acute myocardial infarction in rheumatoid arthritis: a population-based study

OBJECTIVES To determine the effect of glucocorticoids (GCs) on acute myocardial infarction (MI) risk in patients with RA. METHODS Using administrative health data, we conducted a population-based cohort study of 8384 incident RA cases (1997-2006). Primary exposure was incident GC use. MI events were ascertained using hospitalization and vital statistics data. We used Cox proportional-hazards models and modelled GC use as four alternative time-dependent variables (current use, current dose, cumulative dose and cumulative duration), adjusting for demographics, comorbidities, cardiovascular drug use, propensity score and RA characteristics. Sensitivity analyses explored potential effects of unmeasured confounding. RESULTS Within 50 238 person-years in 8384 RA cases, we identified 298 incident MI events. Multivariable models showed that current GC use was associated with 68% increased risk of MI [Hazard ratio (HR) = 1.68, 95% CI 1.14, 2.47]. Similarly, separate multivariable models showed that current daily dose (HR = 1.14, 95% CI 1.05, 1.24 per each 5 mg/day increase), cumulative duration of use (HR = 1.14, 95% CI 1.00, 1.29 per year of GC use) and total cumulative dose (HR = 1.06, 95% CI 1.02, 1.10 per gram accumulated in the past) were also associated with increased risk of MI. Furthermore, in the same multivariable model, current dose and cumulative use were independently associated with an increased risk of MI (10% per additional year on GCs and 13% per 5 mg/day increase). CONCLUSION GCs are associated with an increased risk of MI in RA. Our results suggest a dual effect of GCs on MI risk, an immediate effect mediated through current dosage and a long-term effect of cumulative exposure.

A Genome-Wide Association Study Identifies a Novel Major Locus for Glycemic Control in Type 1 Diabetes, as Measured by Both A1C and Glucose

OBJECTIVE Glycemia is a major risk factor for the development of long-term complications in type 1 diabetes; however, no specific genetic loci have been identified for glycemic control in individuals with type 1 diabetes. To identify such loci in type 1 diabetes, we analyzed longitudinal repeated measures of A1C from the Diabetes Control and Complications Trial. RESEARCH DESIGN AND METHODS We performed a genome-wide association study using the mean of quarterly A1C values measured over 6.5 years, separately in the conventional (n = 667) and intensive (n = 637) treatment groups of the DCCT. At loci of interest, linear mixed models were used to take advantage of all the repeated measures. We then assessed the association of these loci with capillary glucose and repeated measures of multiple complications of diabetes. RESULTS We identified a major locus for A1C levels in the conventional treatment group near SORCS1 (10q25.1, P = 7 × 10−10), which was also associated with mean glucose (P = 2 × 10−5). This was confirmed using A1C in the intensive treatment group (P = 0.01). Other loci achieved evidence close to genome-wide significance: 14q32.13 (GSC) and 9p22 (BNC2) in the combined treatment groups and 15q21.3 (WDR72) in the intensive group. Further, these loci gave evidence for association with diabetic complications, specifically SORCS1 with hypoglycemia and BNC2 with renal and retinal complications. We replicated the SORCS1 association in Genetics of Diabetes in Kidneys (GoKinD) study control subjects (P = 0.01) and the BNC2 association with A1C in nondiabetic individuals. CONCLUSIONS A major locus for A1C and glucose in individuals with diabetes is near SORCS1. This may influence the design and analysis of genetic studies attempting to identify risk factors for long-term diabetic complications.

Flexible modeling of the cumulative effects of time‐dependent exposures on the hazard

Many epidemiological studies assess the effects of time-dependent exposures, where both the exposure status and its intensity vary over time. One example that attracts public attention concerns pharmacoepidemiological studies of the adverse effects of medications. The analysis of such studies poses challenges for modeling the impact of complex time-dependent drug exposure, especially given the uncertainty about the way effects cumulate over time and about the etiological relevance of doses taken in different time periods. We present a flexible method for modeling cumulative effects of time-varying exposures, weighted by recency, represented by time-dependent covariates in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines. We validated the method in simulations and applied it to re-assess the association between exposure to a psychotropic drug and fall-related injuries in the elderly.

CYP2A6 slow nicotine metabolism is associated with increased quitting by adolescent smokers

Variation in the CYP2A6 gene, which decreases the rate of nicotine metabolic inactivation, is associated with higher adult smoking cessation rates during clinical trials. We hypothesized that slow metabolism is associated with increased quitting during adolescence. White adolescent smokers (N=308, aged 12–17, 36.3% male) from a cohort study were genotyped for CYP2A6, resulting in 7.8% slow, 14.0% intermediate and 78.2% normal metabolizers. Overall, 144 smokers quit smoking, as indicated by being abstinent for at least 12 months. In logistic regression analyses, the odds ratio for quitting was 2.25 (95% confidence interval 1.05, 4.80; P=0.037) for slow metabolizers relative to normal metabolizers. A linear trend toward increased quitting with decreasing CYP2A6 activity was also observed (odds ratio=1.44, 95% confidence interval 1.02, 2.01; P=0.034). Thus, CYP2A6 slow metabolism is associated with increased adolescent smoking cessation, indicating that even early in the smoking history, genetic variation is influencing smoking cessation.

Incidence and Determinants of Cigarette Smoking Initiation in Young Adults

PURPOSE To describe the incidence and identify predictors of smoking initiation in young adults. METHODS Data were collected in self-report questionnaires in 22 cycles over 13 years in a prospective cohort investigation of 1,293 students recruited in 1999-2000 from all grade 7 classes in a convenience sample of 10 high schools in Montreal, Canada. Participants were 12.7 years of age on average at cohort inception and 24.0 years of age in cycle 22. Independent predictors of smoking initiation in young adulthood (post-high school) were identified in multivariable logistic regression analysis using generalized estimating equations. RESULTS Of 1,293 participants, 75% initiated smoking by cycle 22. Of these, 44%, 43%, and 14% initiated before high school, during high school, and in the 6 years after high school, respectively. The incidence density rate of initiation was .33, .13, .14, .11, and .12 initiation events per person-year in grade 7, 8, 9, 10, and 11, respectively, and .05 post-high school. Independent predictors of smoking initiation in young adults included alcohol use, higher impulsivity, and poor academic performance. CONCLUSIONS A total of 14% of smokers who initiated smoking before age 24 years did so after high school. The predictors of initiation in young adults may provide direction for relevant preventive interventions.

Comparison of alternative models for linking drug exposure with adverse effects.

Pharmacoepidemiology investigates associations between time-varying medication use/dose and risk of adverse events. Applied research typically relies on a priori chosen simple conventional models, such as current dose or any use in the past 3 months. However, different models imply different risk predictions, and only one model can be etiologically correct in any specific applications. We first formally defined several candidate models mapping the time vector of past drug doses (X (t), t  =  1, … ,u) into the value of a time-varying exposure metric M(u) at current time u. In addition to conventional one-parameter models, we considered two-parameter models accounting for recent dose increase or withdrawal and a flexible spline-based weighted cumulative exposure (WCE) model that defines M(u) as the weighted sum of past doses. In simulations, we generated event times assuming one of the models was correct and then analyzed the data with all candidate models. We demonstrated that the minimum AIC criterion is able to identify the correct model as the best-fitting model or one of the equivalent (within 4 AIC points of the minimum) models in a vast majority of simulated samples, especially with 500 or more events. We also showed how relying on an incorrect a priori chosen model may largely reduce the power to test for an association. Finally, we demonstrated how the flexible WCE estimates may help with model diagnostics even if the correct model is not WCE. We illustrated the practical advantages of AIC-based a posteriori model selection and WCE modeling in a real-life pharmacoepidemiology example.

Assessing the cumulative effects of exposure to selected benzodiazepines on the risk of fall-related injuries in the elderly.

BACKGROUND The use of benzodiazepines is associated with increased risk of fall-related injuries in the elderly. However, it is unclear if the risks vary across the products and how they depend on the pattern of use and dosage. Specifically, the possibility of cumulative effects of past benzodiazepine use has not been thoroughly investigated. METHODS We used the administrative database for a cohort of 23,765 new users of benzodiazepines, aged 65 years and older, in Quebec, Canada, between 1990 and 1994. The associations between the use of seven benzodiazepines and the risk of fall-related injuries were assessed using several statistical models, including a novel weighted cumulative exposure model. That model assigns to each dose taken in the past a weight that represents the importance of that dose in explaining the current risk of fall. RESULTS For flurazepam, the best-fitting model indicated a cumulative effect of doses taken in the last two weeks. Uninterrupted use of flurazepam in the past months was associated with a highly significant increase in the risk of fall-related injuries (HR = 2.83, 95% CI: 1.45-4.34). The cumulative effect of a 30-day exposure to alprazolam was 1.27 (1.13-1.42). For temazepam, the results suggested a potential withdrawal effect. CONCLUSIONS Mechanisms affecting the risk of falls differ across benzodiazepines, and may include cumulative effects of use in the previous few weeks. Thus, benzodiazepine-specific analyses that account for individual patterns of use should be preferred over simpler analyses that group different benzodiazepines together and limit exposure to current use or current dose.

Strategies to increase the demand for childhood vaccination in low- and middle-income countries: a systematic review and meta-analysis

Abstract Objective To investigate which strategies to increase demand for vaccination are effective in increasing child vaccine coverage in low- and middle-income countries. Methods We searched MEDLINE, EMBASE, Cochrane library, POPLINE, ECONLIT, CINAHL, LILACS, BDSP, Web of Science and Scopus databases for relevant studies, published in English, French, German, Hindi, Portuguese and Spanish up to 25 March 2014. We included studies of interventions intended to increase demand for routine childhood vaccination. Studies were eligible if conducted in low- and middle-income countries and employing a randomized controlled trial, non-randomized controlled trial, controlled before-and-after or interrupted time series design. We estimated risk of bias using Cochrane collaboration guidelines and performed random-effects meta-analysis. Findings We identified 11 studies comprising four randomized controlled trials, six cluster randomized controlled trials and one controlled before-and-after study published in English between 1996 and 2013. Participants were generally parents of young children exposed to an eligible intervention. Six studies demonstrated low risk of bias and five studies had moderate to high risk of bias. We conducted a pooled analysis considering all 11 studies, with data from 11 512 participants. Demand-side interventions were associated with significantly higher receipt of vaccines, relative risk (RR): 1.30, (95% confidence interval, CI: 1.17–1.44). Subgroup analyses also demonstrated significant effects of seven education and knowledge translation studies, RR: 1.40 (95% CI: 1.20–1.63) and of four studies which used incentives, RR: 1.28 (95% CI: 1.12–1.45). Conclusion Demand-side interventions lead to significant gains in child vaccination coverage in low- and middle-income countries. Educational approaches and use of incentives were both effective strategies.

Cohort Profile: The Nicotine Dependence in Teens (NDIT) Study

The Nicotine Dependence in Teens (NDIT) study is a prospective cohort investigation of 1294 students recruited in 1999-2000 from all grade 7 classes in a convenience sample of 10 high schools in Montreal, Canada. Its primary objectives were to study the natural course and determinants of cigarette smoking and nicotine dependence in novice smokers. The main source of data was self-report questionnaires administered in class at school every 3 months from grade 7 to grade 11 (1999-2005), for a total of 20 survey cycles during high school education. Questionnaires were also completed after graduation from high school in 2007-08 and 2011-12 (survey cycles 21 and 22, respectively) when participants were aged 20 and 24 years on average, respectively. In addition to its primary objectives, NDIT has embedded studies on obesity, blood pressure, physical activity, team sports, sedentary behaviour, diet, genetics, alcohol use, use of illicit drugs, second-hand smoke, gambling, sleep and mental health. Results to date are described in 58 publications, 20 manuscripts in preparation, 13 MSc and PhD theses and 111 conference presentations. Access to NDIT data is open to university-appointed or affiliated investigators and to masters, doctoral and postdoctoral students, through their primary supervisor (www.nditstudy.ca).