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Leas, EC, Pierce JP, Benmarhnia T, White MM, Noble ML, Trinidad DR, Strong DR.  2018.  Effectiveness of pharmaceutical smoking cessation aids in a nationally representative cohort of American smokers. Jnci-Journal of the National Cancer Institute. 110:581-587.   10.1093/jnci/djx240   AbstractWebsite

Background: Despite strong efficacy in randomized trials, the population effectiveness of pharmaceutical aids in long-term smoking cessation is lacking, possibly because of confounding (factors that are associated with both pharmaceutical aid use and difficulty quitting). Matching techniques in longitudinal studies can remove this confounding bias. Methods: Using the nationally representative Tobacco Use Supplement to the Current Population Survey (TUS-CPS), we assessed the effectiveness of medications to aid quitting among baseline adult smokers who attempted to quit prior to one year of follow-up in two longitudinal studies: 2002-2003 and 2010-2011. Pharmaceutical aid users and nonusers with complete data (n = 2129) were matched using propensity score models with 12 potential confounders (age, sex, race-ethnicity, education, smoking intensity, nicotine dependence, previous quit history, self-efficacy to quit, smoke-free homes, survey year, and cessation aid use). Using matched data sets, logistic regression models were fit to assess whether use of any individual pharmaceutical aid increased the proportion of patients who were abstinent for 30 days or more at follow-up. Results: Propensity score matching markedly improved balance on the potential confounders between the pharmaceutical aid use groups. Using matched samples to provide a balanced comparison, there was no evidence that use of varenicline (adjusted risk difference [aRD] = 0.01, 95% confidence interval (CI] = -0.07 to Oil), bupropion (aRD = 0.02, 95% CI = -0.04 to 0.09), or nicotine replacement (aRD = 0.01, 95% CI = -0.03 to 0.06) increased the probability of 30 days or more smoking abstinence at one-year follow-up. Conclusions: The lack of effectiveness of pharmaceutical aids in increasing long-term cessation in population samples is not an artifact caused by confounded analyses. A possible explanation is that counseling and support interventions provided in efficacy trials are rarely delivered in the general population.

Lewin, A, Brondeel R, Benmarhnia T, Thomas F, Chaix B.  2018.  Attrition bias related to missing outcome data: A longitudinal simulation study. Epidemiology. 29:87-95.   10.1097/ede.0000000000000755   AbstractWebsite

Background: Most longitudinal studies do not address potential selection biases due to selective attrition. Using empirical data and simulating additional attrition, we investigated the effectiveness of common approaches to handle missing outcome data from attrition in the association between individual education level and change in body mass index (BMI). Methods: Using data from the two waves of the French RECORD Cohort Study (N = 7,172), we first examined how inverse probability weighting (IPW) and multiple imputation handled missing outcome data from attrition in the observed data (stage 1). Second, simulating additional missing data in BMI at follow-up under various missing-at-random scenarios, we quantified the impact of attrition and assessed how multiple imputation performed compared to complete case analysis and to a perfectly specified IPW model as a gold standard (stage 2). Results: With the observed data in stage 1, we found an inverse association between individual education and change in BMI, with complete case analysis, as well as with IPW and multiple imputation. When we simulated additional attrition under a missing-at-random pattern (stage 2), the bias increased with the magnitude of selective attrition, and multiple imputation was useless to address it. Conclusions: Our simulations revealed that selective attrition in the outcome heavily biased the association of interest. The present article contributes to raising awareness that for missing outcome data, multiple imputation does not do better than complete case analysis. More effort is thus needed during the design phase to understand attrition mechanisms by collecting information on the reasons for dropout.

Loizeau, M, Buteau S, Chaix B, McElroy S, Counil E, Benmarhnia T.  2018.  Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility? Environmental Research. 167:650-661.   10.1016/j.envres.2018.08.002   AbstractWebsite

Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991-2002 were included as cases. Daily ozone concentration estimates at partictaipants' residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income ( < 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74-0.76] to 1.01 [1.00-1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96-1.05] to 1.02 [0.97-1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility.