The burden and experiences that come with a breast cancer diagnosis in a family impact how women perceive personal cancer risk and pursue preventive strategies and/or early detection screening. Hence, this study sought to understand how Filipino women incorporate their experiences living with a sister diagnosed with early-onset breast cancer to their personal perceived risk and screening behavior. Guided by phenomenological approach of inquiry, a face-to-face, semi-structured interview was conducted with 12 purposively sampled women with a female sibling diagnosed with breast cancer before age 50. Transcripts were analyzed using thematic analysis. Results revealed that the respondents tend to compare themselves with their sister when constructing views of personal cancer vulnerability. The subjective risk is also shaped by their beliefs regarding cancer causation such as personalistic causes, personal theory of inheritance, and locus of control. Their sisters' cancer diagnoses serve as a motivation for them to perform breast self-examination. However, clinical breast examination and screening mammography are underutilized due to perceived barriers such as difficulty allotting time to medical consultation, fear, and lack of finances. Overall, cancer risk perception and screening behavior are important factors that must be addressed during cancer genetic counseling consultations. Better understanding of these factors will aid in the formulation of an effective management plan for at-risk women.

No abstract

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China.  Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility

COVID-19 is a disease caused by the novel coronavirus now known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).The first human cases were reported at Wuhan, China, in December of 2019 and subsequently spread to nearly every country leading the World Health Organization (WHO) to declare the outbreak as a pandemic or a Public Health Emergency of International Concern (WHO, 2020). The COVID-19 pandemic poses a significant challenge to healthcare professionals and health systems around the world, most notably the disruption of service delivery. Genetic counselors (GCs), as part of the healthcare team, play an important role in providing genetic services by helping patients and/or families "understand and adapt to the medical, psychological, and familial implications of genetic contributions to disease", (Resta et al., 2006). Genetics and genetic counseling have become integral to health care as we learn more about genetic risks for disease (Bruder, 2020). The typical work setting for most GCs is in a clinic or hospital. However, during the COVID-19 pandemic, to help prevent the further spread of the virus, clinics and hospitals have restricted in-person delivery of healthcare services that are not deemed urgent, including genetic counseling. Patients' access to genetic counseling services has thus been limited, which prompted GCs to utilize an alternative way to provide counseling through telegenetics. An article from Pagliazzi et al. (2020) described their genetic counseling experience in Tuscany, Italy, when the pandemic hit. It was widely known that Italy was one of the hardest-hit countries during the pandemic. A pediatric tertiary hospital in Tuscany stratified their scheduled visits and assessed which cases would be seen via faceto-face and which would be seen via telemedicine. All prenatal visits were face to face since the patient still had to go the hospital for