Associations of Maternal Use of Benzodiazepines or Benzodiazepine-like Hypnotics During Pregnancy With Immediate Pregnancy Outcomes in Norway.

Importance: Understanding the safety profile of medications used in pregnancy is crucial for clinical decision-making. Few studies exist on the associations of exposure to benzodiazepines and benzodiazepine-like hypnotic drugs (z-hypnotics) in pregnancy with pregnancy outcomes. Objective: To determine whether exposure to benzodiazepines or z-hypnotics in pregnancy is associated with greater risk of negative immediate pregnancy outcomes compared with nonexposure. Design, Setting, and Participants: This questionnaire-based cohort study used data from the Norwegian Mother, Father and Child cohort study (MoBa), which also includes data from the Medical Birth Registry of Norway. Pregnant women were recruited from all over Norway from 1999 and 2008. The first child was born in October 1999 and the last in July 2009. This analysis included women who completed 3 questionnaires, twice during pregnancy and once 6 months after delivery. Data analyses were conducted from September to November 2019. Exposures: Self-reported exposure to benzodiazepines or z-hypnotics during pregnancy, characterized in terms of any exposure, timing (ie, early, middle, or late), and duration of exposure. Main Outcomes and Measures: The main outcomes were gestational age at delivery, risk of preterm delivery, birth weight, birth weight relative to gestational age and sex, risk of being small for gestational age, head circumference, Apgar score less than 7 at 5 minutes, and risk of neonatal respiratory distress. Continuous outcomes are reported using effect estimates as mean differences, and binary outcomes are reported using risk ratios. Results: The MoBa study included 114 234 mother-child dyads. This analysis of MoBa data includes 82 038 singleton pregnancies among 69 434 unique women. Mean (SD) maternal age was 30.2 (4.5) years, and 37 641 pregnancies (45.9%) were in primiparous women. Exposure to benzodiazepines or z-hypnotics was reported in 679 pregnancies (0.8%). After adjusting for all measured baseline and postbaseline confounders, benzodiazepine or z-hypnotic use during pregnancy was associated with lower birth weight (mean difference, -79.3 [95% CI, -126.7 to -31.9] g), lower gestational age at birth (mean difference, -2.1 [95% CI, -3.3 to -0.9] days), and higher risk of preterm birth (risk ratio, 1.41 [95% CI, 1.03 to 1.94]). We found no significant association of exposure to benzodiazepines or z-hypnotics with the child's birth weight relative to gestational age and sex (z score), or any of the other immediate birth outcomes. Conclusions and Relevance: These findings suggest that the magnitude of the association of exposure to benzodiazepines or z-hypnotics with gestational age is not necessarily clinically significant. The absence of an association of exposure to benzodiazepines or z-hypnotics with z score for birth weight relative to gestational age and sex suggests that association of exposure to benzodiazepines or z-hypnotics with birth weight could be explained by earlier delivery rather than impaired intrauterine growth.

Effect heterogeneity and variable selection for standardizing causal effects to a target population.

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in an unbiased estimate of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.

On the collapsibility of measures of effect in the counterfactual causal framework.

The relationship between collapsibility and confounding has been subject to an extensive and ongoing discussion in the methodological literature. We discuss two subtly different definitions of collapsibility, and show that by considering causal effect measures based on counterfactual variables (rather than measures of association based on observed variables) it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure, from the components that are due to structural bias such as confounding. We provide new weights such that the causal risk ratio is collapsible over arbitrary baseline covariates. In the absence of confounding, these weights may be used for standardization of the risk ratio.

The choice of effect measure for binary outcomes: Introducing counterfactual outcome state transition parameters.

Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this paper, we introduce a new framework for reasoning about choice of effect measure by linking two separate versions of the risk ratio to a counterfactual causal model. In our approach, effects are defined in terms of "counterfactual outcome state transition parameters", that is, the proportion of those individuals who would not have been a case by the end of follow-up if untreated, who would have responded to treatment by becoming a case; and the proportion of those individuals who would have become a case by the end of follow-up if untreated who would have responded to treatment by not becoming a case. Although counterfactual outcome state transition parameters are generally not identified from the data without strong monotonicity assumptions, we show that when they stay constant between populations, there are important implications for model specification, meta-analysis, and research generalization.

Comparative Effectiveness Research Using Observational Data: Active Comparators to Emulate Target Trials with Inactive Comparators.

INTRODUCTION: Because a comparison of noninitiators and initiators of treatment may be hopelessly confounded, guidelines for the conduct of observational research often recommend using an "active" comparator group consisting of people who initiate a treatment other than the medication of interest. In this paper, we discuss the conditions under which this approach is valid if the goal is to emulate a trial with an inactive comparator. IDENTIFICATION OF EFFECTS: We provide conditions under which a target trial in a subpopulation can be validly emulated from observational data, using an active comparator that is known or believed to be inactive for the outcome of interest. The average treatment effect in the population as a whole is not identified, but under certain conditions this approach can be used to emulate a trial in the subset of individuals who were treated with the treatment of interest, in the subset of individuals who were treated with the treatment of interest but not with the comparator, or in the subset of individuals who were treated with both the treatment of interest and the active comparator. THE PLAUSIBILITY OF THE COMPARABILITY CONDITIONS: We discuss whether the required conditions can be expected to hold in pharmacoepidemiologic research, with a particular focus on whether the conditions are plausible in situations where the standard analysis fails due to unmeasured confounding by access to health care or health seeking behaviors. DISCUSSION: The conditions discussed in this paper may at best be approximately true. Investigators using active comparator designs to emulate trials with inactive comparators should exercise caution.

Incentives and participation in a medical survey. / Incentiver og deltagelse i en medisinsk spørreundersøkelse.

BACKGROUND Questionnaire surveys are important for surveying the health and disease behaviour of the population, but recent years have seen a fall in participation. Our study tested whether incentives can increase participation in these surveys.MATERIAL AND METHOD We sent a questionnaire on risk factors for colorectal cancer (height, weight, smoking, self-reported diagnoses, family medical history) to non-screened participants in a randomised colonoscopy screening study for colorectal cancer: participants who were invited but did not attend for colonoscopy examination (screening-invited) and persons who were not offered colonoscopy (control group). The persons were randomised to three groups: no financial incentive, lottery scratch cards included with the form, or a prize draw for a tablet computer when they responded to the form. We followed up all the incentive groups with telephone reminder calls, and before the prize draw for the tablet computer.RESULTS Altogether 3 705 of 6 795 persons (54.5 %) responded to the questionnaire; 43.5 % of those invited for screening and 65.6 % of the control group (p < 0.001). The proportion that answered was not influenced by incentives, either among those invited for screening (42.4 % in the non-prize group, 45.5 % in the lottery scratch card group and 42.6 % in the prize draw group; p = 0.24), or in the control group (65.6 % in the non-prize group, 66.4 % in the lottery scratch card group and 64.7 % in the prize draw group; p = 0.69). Prior to reminder calls, 39.2 % responded. A further 15.3 % responded following telephone reminder calls (14.1 % of the screening-invited and 16.5 % of the control group; p < 0.001).INTERPRETATION Incentives did not increase participation in this medical questionnaire survey. Use of telephone reminder calls and telephone interviews increased participation, but whether this is more effective than other methods requires further study.

Methods to Estimate the Comparative Effectiveness of Clinical Strategies that Administer the Same Intervention at Different Times.

Clinical guidelines that rely on observational data due to the absence of data from randomized trials benefit when the observational data or its analysis emulates trial data or its analysis. In this paper, we review a methodology for emulating trials that compare the effects of different timing strategies, that is, strategies that vary the frequency of delivery of a medical intervention or procedure. We review trial emulation for comparing (i) single applications of the procedure at different times, (ii) fixed schedules of application, and (iii) schedules adapted to the evolving clinical characteristics of the patients. For illustration, we describe an application in which we estimate the effect of surveillance colonoscopies in patients who had an adenoma detected during the Norwegian Colorectal Cancer Prevention (NORCCAP) trial.