Job loss during pregnancy and the risk of miscarriage and stillbirth.

STUDY QUESTION: Does the exposure to job loss during pregnancy increase the risk of miscarriage or stillbirth? SUMMARY ANSWER: The experience of own or partner's job loss during the pregnancy is associated with an increased risk of miscarriageand stillbirth. WHAT IS KNOWN ALREADY: Prior research on the psycho-social aspect of pregnancy loss has investigated the contextual and the individual-level stressors. At the contextual level, natural disasters, air pollution, and economic downturns are associated with higher risk of pregnancy loss. At the individual level, intense working schedules and financial strain are linked with increased risk of pregnancy loss both at early and later stages of the gestation. STUDY DESIGN, SIZE, DURATION: This work draws on high-quality individual data of 'Understanding Society', a longitudinal survey that has interviewed a representative sample of households living in the UK annually since 2009. Approximately 40 000 households were recruited. The analyses use all the available survey waves (1-12, 2009-2022). PARTICIPANTS/MATERIALS, SETTING, METHODS: The final sample consisted of 8142 pregnancy episodes that contain complete informationon pregnancy outcome and date of conception. Ongoing pregnancies at the time of the interview were excluded from the final sample. The outcome variable indicated whether a pregnancy resulted in a live birth or a pregnancy loss whereas the exposure variable identified the women's or their partner's job loss because of redundancy or a dismissal. Logistic regression models were employed to estimate the relation between job loss during pregnancy and pregnancy loss. The models were adjusted for an array of socio-demographic and economic characteristics following a stepwise approach. Several sensitivity analyses complemented the main findings. MAIN RESULTS AND THE ROLE OF CHANCE: Baseline models controlling for women's demographic background and prior experience of miscarriage estimated an increased risk of pregnancy loss when women were exposed to their own or their partner's job loss during their pregnancy (odds ratio (OR) = 1.99, 95% CI: 1.32, 2.99). When the models were adjusted for all socio-economic and partnership-related covariates the association remained robust (OR = 1.81, 95% CI: 1.20, 2.73). LIMITATIONS, REASONS FOR CAUTION: First, the pregnancy outcome and the date of conception were self-reported and may besubjected to recall and social desirability bias. Second, although we adjusted for an array socio-demographic characteristics and self-reported health, other contextual factors might be correlated with both job loss and pregnancy loss. Third, owing to the limited sample size, we could not assess if the main finding holds across different socio-economic strata. WIDER IMPLICATIONS OF THE FINDINGS: By showing that exposure to a job loss during pregnancy increases the risk of miscarriage and stillbirth, we underline the relevance of pregnancy loss as a preventable public health matter. This result also calls for policy designthat enhances labour market protection and social security buffers for pregnant women and their partners. STUDY FUNDING/COMPETING INTERESTS: The authors received the following financial support for the research, authorship, and/or publication of this article: H2020 Excellent Science, H2020 European Research Council, Grant/Award Number: 694262 (project DisCont-Discontinuities in Household and Family Formation) and the Economic and Social Research Centre on Micro-Social Change (MiSoC). There are no conflicts of interest to declare.

Loneliness during the Pregnancy-Seeking Process: Exploring the Role of Medically Assisted Reproduction.

This study explores whether undergoing medically assisted reproduction (MAR) is associated with experiencing loneliness and whether this association varies by gender and having a live birth. Using two waves of the Generations and Gender Survey (n = 2,725) from countries in Central and Eastern Europe, we estimate the changes in levels of emotional and social loneliness among pregnancy seekers in heterosexual relationships and test if they vary by the mode of conception while controlling for individual sociodemographic characteristics. Individuals who underwent MAR experienced increased levels of social loneliness compared to individuals who were trying to conceive spontaneously. This association is entirely driven by respondents who did not have a live birth between the two observation periods, while the results did not differ by gender. No differences emerged in emotional loneliness. Our findings suggest that increased social loneliness during the MAR process might be attributable to infertility-related stress and stigma.

Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After the COVID-19 Outbreak.

Most social phenomena are inherently complex and hard to measure, often due to under-reporting, stigma, social desirability bias, and rapidly changing external circumstances. This is for instance the case of Intimate Partner Violence (IPV), a highly-prevalent social phenomenon which has drastically risen in the wake of the COVID-19 pandemic. This paper explores whether big data-an increasingly common tool to track, nowcast, and forecast social phenomena in close-to-real time-might help track and understand IPV dynamics. We leverage online data from Google Trends to explore whether online searches might help reach "hard-to-reach" populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV-both potential threat and actual violent cases-in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results suggest that online searches using selected keywords measuring different facets of IPV are a powerful tool to track potential threats of IPV before and during global-level crises such as the current COVID-19 pandemic, with stronger predictive power post outbreaks. Conversely, online searches help predict actual violence only in post-outbreak scenarios. Our findings, validated by a Facebook survey, also highlight the important role that socioeconomic status (SES) plays in shaping online search behavior, thus shedding new light on the role played by third-level digital divides in determining the predictive power of digital traces. More specifically, they suggest that forecasting might be more reliable among high-SES population strata. Supplementary Information: The online version contains supplementary material available at 10.1007/s10680-022-09619-2.