Joint SOGC/CAR Policy Statement on Non-medical Use of Fetal Ultrasound

An ethical exploration of pregnancy related mHealth: does it deliver?

Many pregnant women use pregnancy related mHealth (PRmHealth) applications, encompassing a variety of pregnancy apps and wearables. These are mostly directed at supporting a healthier fetal development. In this article we argue that the increasing dominance of PRmHealth stands in want of empirical knowledge affirming its beneficence in terms of improved pregnancy outcomes. This is a crucial ethical issue, especially in the light of concerns about increasing pressures and growing responsibilities ascribed to pregnant women, which may, in turn, be reinforced by PRmHealth. A point can be made that it would be ethically askew if PRmHealth does not lead to improved pregnancy outcomes, while at the same time increasing maternal duties to closely monitor fetal development. We conclude that more research is needed to get a view on the benefits and burdens of PRmHealth in order to ethically assess whether the latter are proportionate to the former. If not, there is a case in saying that endorsement of PRmHealth is overdemanding.

Estimating recurrence and incidence of preterm birth subject to measurement error in gestational age: A hidden Markov modeling approach

Prediction of preterm birth as well as characterizing the etiological factors affecting both the recurrence and incidence of preterm birth (defined as gestational age at birth ≤ 37 wk) are important problems in obstetrics. The National Institute of Child Health and Human Development (NICHD) consecutive pregnancy study recently examined this question by collecting data on a cohort of women with at least 2 pregnancies over a fixed time interval. Unfortunately, measurement error due to the dating of conception may induce sizable error in computing gestational age at birth. This article proposes a flexible approach that accounts for measurement error in gestational age when making inference. The proposed approach is a hidden Markov model that accounts for measurement error in gestational age by exploiting the relationship between gestational age at birth and birth weight. We initially model the measurement error as being normally distributed, followed by a mixture of normals that has been proposed on the basis of biological considerations. We examine the asymptotic bias of the proposed approach when measurement error is ignored and also compare the efficiency of this approach to a simpler hidden Markov model formulation where only gestational age and not birth weight is incorporated. The proposed model is compared with alternative models for estimating important covariate effects on the risk of subsequent preterm birth using a unique set of data from the NICHD consecutive pregnancy study.