BackgroundOptimal breastfeeding has benefits for the mother-infant dyads. This study investigated the prevalence and determinants of cessation of exclusive breastfeeding (EBF) in the early postnatal period in a culturally and linguistically diverse population in Sydney, New South Wales, Australia.MethodsThe study used routinely collected perinatal data on all live births in 2014 (N = 17,564) in public health facilities in two Local Health Districts in Sydney, Australia. The prevalence of mother’s breastfeeding intention, skin-to-skin contact, EBF at birth, discharge and early postnatal period (1–4 weeks postnatal) were estimated. Multivariate logistic regression models that adjusted for confounders were conducted to determine association between cessation of EBF in the early postnatal period and socio-demographic, psychosocial and health service factors.ResultsMost mothers intended to breastfeed (92%), practiced skin-to-skin contact (81%), exclusively breastfed  at delivery (90%) and discharge (89%). However, the prevalence of EBF declined (by 27%) at the early postnatal period (62%). Younger mothers (<20 years) and mothers who smoked cigarettes in pregnancy were more likely to cease EBF in the early postnatal period compared to older mothers (20–39 years) and those who reported not smoking cigarettes, respectively [Adjusted Odds Ratio (AOR) =2.7, 95%CI 1.9–3.8, P <0.001 and AOR = 2.5, 95%CI 2.1–3.0, P <0.001, respectively]. Intimate partner violence, assisted delivery, low socio-economic status, pre-existing maternal health problems and a lack of partner support were also associated with early cessation of EBF in the postnatal period.ConclusionsOur findings suggest that while most mothers intend to breastfeed, and commence EBF at delivery and at discharge, the maintenance of EBF in the early postnatal period is sub-optimal. This highlights the need for efforts to promote breastfeeding in the wider community along with targeted actions for disadvantaged groups and those identified to be at risk of early cessation of EBF to maximise impact.

We aimed to determine the associations between ambient air pollution, specifically particulate matter less than or equal to 10 microns and 2.5 microns (PM10 and PM2.5 respectively) and ozone (O3), and stillbirths. We analysed all singleton births between 20–42 weeks gestation in metropolitan Sydney, Australia, from 1997 to 2012. We implemented logistic regression to assess the associations between air pollutants and stillbirth for each trimester and for the entire pregnancy. Over the study period, there were 967,694 live births and 4287 stillbirths. Mean levels of PM10, PM2.5 and O3 for the entire pregnancy were 17.9 µg/m3, 7.1 µg/m3 and 3.2 ppb, respectively. Adjusted odds ratios were generally greater than unity for associations between PM and stillbirths, but none were statistically significant. There were no significant associations between O3 and stillbirths. There was potential effect modification of the PM10 and O3 association by maternal age. We did not find consistent evidence of associations between PM and O3 and stillbirths in Sydney, Australia. More high quality birth cohort studies are required to clarify associations between air pollution and stillbirths.

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e13587 Background: The use of artificial intelligence (AI) and machine learning is becoming more common and is expected to expand further in order to meet the needs of our ever-evolving healthcare system. In oncology, AI and machine learning are already being explored in various applications. Despite AI’s importance, there is sparse formal teaching on AI incorporated into medical schools’ curricula and residency training programs. In this study, we examined the perceptions and knowledge of Canadian oncology residents and fellows with respect to AI technologies. Methods: An electronic, anonymous, questionnaire-based survey was distributed to residents and fellows in medical and radiation oncology programs across Canada. Survey questions spanned areas of demographics, familiarity with AI, personal attitudes towards AI, and perspectives regarding AI use in different specialties. Approval was obtained from the Queen’s Research Ethics Board prior to conducting this study. Mixed-methods statistical analysis is ongoing. Qualitative data will be analyzed using thematic analysis. Univariable and multivariable regressions will be conducted to identify any correlation between perception or knowledge of AI and demographic factors. Results: Fifty-seven participants responded in total. Most residents (67%) agreed or strongly agreed that it was important they learn about AI. Seventy percent indicated that, if given the chance, they would like to learn more about AI, yet the majority of participants (88%) indicated they had not received formalized teaching. Disciplines that were felt to be most associated with AI were radiology (98%), radiation oncology (84%), and pathology (58%). With respect to the field of radiation oncology, 98% of respondents felt that AI had the potential to replace some, most, or all medical activities. A perceived barrier to understanding AI was a lack of knowledge of mathematics and programming (63%). Respondents indicated that their preferred formats for learning about AI would be workshops (78%), lectures (60%), and collaborative activities with other departments (46%). Conclusions: Our results show that Canadian oncology residents’ sense that AI is important and relevant to their area of training. Despite this, they have not received education on these topics. Thus, formalized teaching, such as lectures and workshops, would be perceived as beneficial by most Canadian oncology residents.