Relationship Between Heated Tobacco Products and Placental Abruption: A Prospective Cohort Study Using Online Questionnaire (preprint)

Background Placental abruption (PA) is a critical obstetric complication, with maternal smoking recognized as a key risk factor. Despite the increased use of heated tobacco products (HTPs), the impact of HTPs remains unclear. This study investigated whether pregnant women using HTPs are at a higher risk of PA than non-users.Methods We analyzed data from "the Japan COVID-19 and Society Internet Survey," a prospective, self-reported online survey cohort. Questionnaires were randomly distributed between July 28, 2021, and August 30, 2021. Pregnant respondents in 2021 were invited to complete an additional survey from February 14, 2022, to February 28, 2022. We set the outcome as the absolute risk difference (aRD) and relative risk ratio (rRR) of PA incidence due to smoking HTPs in the first trimester of pregnancy. The sample size included 12 836 participants. We calculated outcomes using a generalized linear model (GLM) and inverse probability of treatment weighting (IPTW). We also performed a Bayesian approach and multiple-bias analysis for sensitivity analysis.Results We found the robust aRD of 0.07 (95% confidence interval (CI): 0.06, 0.09) and the rRR of 11.3 (95% CI: 7.5, 17.0). Multiple bias analyses showed that unmeasurable confounders would need to have at least an rRR = 14 relationship with both exposure and outcome to disprove the observed association. There has not been post hoc analysis or secondary use of data.Conclusion Early pregnancy use of HTPs is associated with an increased risk of PA.

Relationship between regular cervical cancer screening and social background in Japan during the COVID-19 pandemic (preprint)

Background: Among Organization for Economic Co-operation and Development countries, Japan has one of the lowest cervical cancer screening rates. The cancer screening rate has reportedly worsened with the coronavirus disease of 2019 (COVID-19) pandemic. This study investigated the COVID-19 history and socioeconomic background of people who did not undergo on-time cervical cancer screening (CCS) during the two years of COVID-19-related restrictions in Japan. Methods: We used data from the Japan COVID-19 and Society Internet Survey, a nationwide, internet-based, self-report, cohort observational study conducted in 2022. The outcome variable was identified by asking whether the respondent had undergone on-time CCS within the last two years. This study used multivariate log-binomial regression models to evaluate inequalities during regular checkups for CCS. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were estimated to incorporate socioeconomic background variables. Results: Of 12,066 respondents, 6469 (53.6%) had yet to undergo CCS within two years. The prevalence ratio (PR) of on-time CCS was 0.70 (95% CI: 0.63–0.79) for those in their 20s and 0.78 (95%CI:0.70-0.87) for those in their 60s, compared to their 40s. Moreover, socioeconomic inequities were found in the following groups: unemployed/student, unmarried, high school graduate or lower, and household income below 4 million yen. PR of on-time CCS with a history of COVID-19, unvaccinated status, or fear of COVID-19 was not significantly different from that of respondents who underwent CCS. However, the PR of those who had not undergone on-time CCS was significantly lower than that of respondents who had undergone breast cancer screening (PR: 0.29, 95% CI: 0.28–0.31). Conclusion: the relationship between socioeconomic inequalities and CCS hesitancy was prevalent among the younger respondents. Our findings will help policymakers identify problems and strategies to improve CCS screening rates in Japan.

Partner Relationships, Hopelessness, and Health Status Strongly Predict Maternal Well-Being During the COVID-19 Pandemic: A Machine Learning Approach (preprint)

No recent studies have explicitly focused on predicting the well-being of pregnant women during the novel coronavirus disease (COVID-19) pandemic. This study used data from an extensive online survey in Japan to examine predictors of the subjective well-being of pregnant women during the COVID-19 pandemic. We developed and validated a machine learning model using data from 400 pregnant women obtained in 2020 to identify three factors that predict subjective well-being. The results confirmed that the model could predict pregnant women's subjective well-being with 84% accuracy. The variables that contributed significantly to this prediction were "partner help," "hopelessness," and "health status." The machine learning model was built again using these three factors, trained and validated using data from 400 pregnant women in 2020, and predicted using data from 1,791 pregnant women in 2021, with an accuracy of 88%. These were also significant risk factors for subjective well-being in regression analysis adjusted for maternal age, region, parity, education level, and presence of mental illness. This model would help identify pregnant women with low subjective well-being during the COVID-19 pandemic, and appropriate interventions can then be initiated.