Bisphenols are endocrine disruptors that may be associated with altered fetal growth in humans, and they have similar biological functions to mimic hormones. In addition, aggregated chemicals showed an adverse effect although individual concentration was at a low level. However, most studies between bisphenols and birth outcomes have focused on the effect of individual bisphenol. Thus, we explored the associations of urinary bisphenol mixtures with birth outcomes. We conducted a prospective birth cohort study in South Korea. One hundred eighty mother-infant pairs were recruited from 2017 to 2019. Bisphenol A (BPA), bisphenol F (BPF), and bisphenol S (BPS) in one spot urine were analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. We used two statistical approaches to examine potential associations of BPA, BPF, and BPS with birth weight and gestational age: (1) multivariable linear regression; (2) Bayesian kernel machine regression (BKMR). The geometric means of BPA, BPF, and BPS were 2.1, 0.2, and 0.1 μg/L, respectively. In stratified linear analyses by each median value, a higher BPF was positively associated with birth weight (g) (β = 125.5; 95% CI: 45.0 to 205.9). Mixture analyses using BKMR suggested an inverse association between bisphenol mixtures and birth weight. Our findings suggest that in utero bisphenol exposure may influence birth weight and that such relationships may differ considering non-linearity and the combined effect.
Background: Cancer is a major life event that imposes huge economic and mental burdens on patients and families. In addition, the diagnosis of cancer also causes significant family discordance that can lead to marital problems such as divorce or separation. The aim of this study was to investigate the association and any related gender differences between cancer diagnosis and marital disruption among cancer survivors. Materials and Methods: We used the recent cross-sectional Korea National Health and Nutrition Examination Survey (4 th and 5 th ; Years 2008-2012). The study participants were 623 married cancer survivors over the age of 19. A multivariate logistic regression analysis was conducted to estimate odds ratios. Results: After adjusting for socioeconomic status and health-related behaviors, the odds ratio of marital disruption among female cancer survivors compared with male cancer survivors was 3.94 (95%CI 1.30-11.94; p=0.02). The odds ratio of marital disruption for the below-average economic level compared with the above-average economic level was 5.64 (95%CI: 1.03-31.02; p=0.05). When compared with the non-smoking cancer survivors, the smoking cancer survivors had an OR of marital disruption equal to 2.94 (95%CI: 1.08-8.00; p=0.03). Conclusions: The findings of this study suggest that the odds of marital disruption among female cancer survivors are higher than those among their male couterparts. Medical practitioners should be sensitive to early signs of marital discord in couples affected by a cancer diagnosis. Early identification and psychosocial intervention might reduce the frequency of divorce and separation and thus improve quality of life and quality of care for cancer survivors.
To identify population-based cancer indicators and construct monitoring systems for the entire lifecycle of cancer patients using a modified Delphi method. A modified Delphi method was used to identify the cancer indicators and measurement by scoping review and gray literature. The final list of cancer indicators was developed by consensus of 11 multidisciplinary experts over multiple rounds and rating scored the importance of each indicator on a 10-point scale. Frequency analysis was performed to rate with median scores ≥7 and finalized the list of indicators according to the priority. Initially, 254 indicators were identified, of which 94 were considered important and feasible. After two rounds of rating by the experts and panel discussions, 26 indicators were finalized in six domains: primary prevention (n = 7), secondary prevention (n = 11), treatment (n = 2), quality of life (n = 4), survivor management (n = 1), and end-of-life care (n = 1). The Donabedian model used for examining health services and the Institute of Medicine quality of healthcare domains were applied to the measurement system. Panel experts identified cancer indicators based on priorities with a high level of consensus, providing a scrupulous foundation for community-based monitoring of cancer patients.
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