Low perceived susceptibility to pregnancy as a reason for contraceptive nonuse among women with unintended births

By: Alison Gemmill, & Sarah K. Cowan

Published in: Demographic Research 44 (2021)

Background: While low perceived susceptibility (PS) to pregnancy is a common risk factor for having sex without contraception among women susceptible to unintended pregnancy, little research has examined the correlates of low PS, and none have investigated whether low PS predisposes women to later pregnancy discovery and prenatal care initiation among women with unintended births.

Methods: We use data from the 2004‒2011 Pregnancy Risk Assessment Monitoring System and limit our sample to women in the United States with unintended births who were not using contraception at the time of the index pregnancy (n = 55,940). Women were classified as having low PS if they indicated they could not get pregnant at the time the index pregnancy occurred or they or their partner were sterile. We use logistic regression to identify correlates of low PS and determine whether low PS is associated with timing of pregnancy recognition and prenatal care initiation.

Results: Over one-third of women with unintended births cited low PS as a reason for contraceptive nonuse. Maternal age and disadvantage are correlated with low PS. Among women with unintended births, those with low PS had lower odds of early pregnancy recognition (adjOR = 0.88; 95% CI: 0.82, 0.94) and prenatal care initiation (adjOR = 0.86; 95% CI: 0.79, 0.94) compared to those who did not hold these beliefs.

Contribution: Although research remains focused on other barriers to contraceptive use, low perceived susceptibility to pregnancy is critical to understanding the high rates of unintended pregnancies and births in the United States and may affect prenatal health.

This research is the winner of the Editor’s Choice Designation from Demographic Research.

Secrets and Social Networks

By: Sarah K. Cowan

Published in: Current Opinion in Psychology 31 (2020)

Secrets are information kept from others; they are relational. They shape the intimacy of our relationships, what we know of others and what we infer about the world. Recent research has promoted two models of voluntary secret disclosure. The first highlights deliberate and strategic disclosure to garner support and to avoid judgment. The second maintains strategic action but foregrounds that disclosures are made in contexts which shape who is in one’s social network and who may be the recipient of a disclosure. Work outside of this main vein examines the mechanisms and motivations to share others’ secrets as well as the potential consequences of doing so. The final avenue of inquiry in this review considers how keeping secrets can change (or avoid changing) the size and composition of the secret-keeper’s social network and what information is shared within it. Understanding how secrets spread within and form social networks informs work from public health to criminology to organizational management.

Estimating Personal Network Size with Non-random Mixing via Latent Kernels

By: Swupnil Sahai, Timothy Jones*, Sarah K. Cowan & Tian Zheng

Published in: Aiello L., Cherifi C., Cherifi H., Lambiotte R., Lió P., Rocha L. (eds) Complex Networks and Their Applications VII. Complex Networks 2018. Studies in Computational Intelligence, vol 812. Springer.

A major problem in the study of social networks is estimating the number of people an individual knows. However, there is no general method to account for barrier effects, a major source of bias in common estimation procedures. The literature describes approaches that model barrier effects, or non-random mixing, but they suffer from unstable estimates and fail to give results that agree with specialists’ knowledge. In this paper we introduce a model that builds off existing methods, imposes more structure, requires significantly fewer parameters, and yet allows for greater interpretability. We apply our model on responses gathered from a survey we designed and show that our conclusions better match what sociologists find in practice. We expect that this approach will provide more accurate estimates of personal network sizes and hence remove a significant hurdle in sociological research.