Maternity Nurses' Knowledge about Sudden Unexpected Postnatal Collapse and Safe Newborn Positioning

Addison, Camilla; Ludington‐Hoe, Susan M.

doi:10.1097/nmc.0000000000000597

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Knowledge of SUPC by maternity nurses has been shown to be poor (Addison & Ludington-Hoe, 2020). Although SUPC is rare, it is important for midwives and maternity nurses to be aware of the factors associated with SUPC, so they can mitigate the risk (Association of Women's Health, Obstetric and Neonatal Nurses [AWHONN], 2020).…”

Section: Clinical Nursing Implicationsmentioning

confidence: 99%

Sudden Unexpected Postnatal Collapse Resulting in Newborn Death in the United States

Anderson

Ferres

Ramirez

et al. 2021

McN: The American Journal of Maternal/Child Nursing

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Background: The sudden collapse of an apparently healthy newborn, or sudden unexpected postnatal collapse (SUPC) is fatal in about half of cases. Epidemiological characteristics of sudden unexpected infant death (SUID) in the fi rst week of life differ from those in the postperinatal age group (7-365 days). Aim: To describe the characteristics of SUPC resulting in neonatal death. Methods: We analyzed the Centers for Disease Control and Prevention Birth Cohort Linked Birth/Infant Death Data Set (2003-2013: 41,125,233 births and 37,624 SUIDs). SUPC was defi ned as infants born ≥35 weeks gestational age, with a 5-minute Apgar score of ≥7, who died suddenly and unexpectedly in the fi rst week of life. Results: Of the 37,624 deaths categorized as SUID during the study period, 616 met the SUPC criteria (1.5/100,000 live births). Eleven percent occurred on the fi rst day of life and nearly three quarters occurred during postnatal days 3-6. SUPC deaths differed statistically from SUID deaths occurring 7-364 days of age, in particular for sex, marital status, and live birth order. Implications: These data support the need for adequate nurse staffi ng during the immediate recovery period and for the entire postpartum stay as well as nurse rounding for new mothers in the hospital setting.

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Section: Clinical Nursing Implicationsmentioning

confidence: 99%

Sudden Unexpected Postnatal Collapse Resulting in Newborn Death in the United States

Anderson

Ferres

Ramirez

et al. 2021

McN: The American Journal of Maternal/Child Nursing

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“…The purpose of this quality improvement project was to implement a safe sleep initiative in a hospital setting. The primary goals of the initiative were to improve nurses' adherence to safe sleep recommendations and to increase role modeling of a safe infant sleep environment (Addison & Ludington-Hoe, 2020; Anderson et al, 2021). Nursing practice changes can affect parents using safe sleep practices at home after hospital discharge and, therefore, infant safety and prevention of sleep-related death.…”

mentioning

confidence: 99%

Infant Safe Sleep Initiative in a Small Volume Maternity Service

Patterson

Adams

Ramieh

2022

McN: The American Journal of Maternal/Child Nursing

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Purpose: Despite the 2016 American Academy of Pediatrics recommendations for preventing sudden unexpected infant death, there are approximately 3,400 sudden unexpected infant deaths annually in the United States. Modeling of safe sleep practices by nurses in the hospital influences parents' practice at home; however, safe sleep practices are not consistently modeled during the childbirth hospitalization.Methods: A quality improvement project was conducted to implement a safe sleep initiative based on the 2016 American Academy of Pediatrics recommendations for nurses caring for hospitalized infants after birth. Evaluation included testing of nurses' knowledge, infant crib audits, and nurses' evaluation of the intervention.Results: A significant improvement was found in overall nurse education scores. Crib audits demonstrated a significant improvement in the following elements: use of multiple blankets, swaddling of the infant, and parent teaching. Nursing surveys reported an increase in confidence to practice safe sleep recommendations and educate and redirect parents.Clinical Implications: Implementing a safe sleep initiative can increase nurses' knowledge, improve adherence to recommendations with modeling safe sleep practices, and increase parent awareness of safe sleep recommendations, potentially positively affecting adherence after discharge.

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Section: Benefits Of Breastfeeding Are Well Established (American Aca...mentioning

confidence: 99%

AI methods and their application to health and prevention using open data.

Ferres¹

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Artificial Intelligence (AI) and machine learning have revolutionized society, impacting everything from search engines and media consumption to loan approval processes. These algorithms are powered by data and can solve problems that have been impossible for humans to solve. In the health sector, the use of AI has been growing exponentially, and investments in data collection and open data are crucial for its progress. The thesis discussed in the article uses ML models on open data sets to address various health-related questions. In chapter 1, the focus is on sudden infant death syndrome (SIDS) and sudden unexpected infant death (SUID) and the effect of maternal smoking during pregnancy. By using a glass-box model known as generalized additive models (GAMs), the authors found that SUID risk doubles with any maternal smoking during pregnancy, and each cigarette increases the odds by 0.07, with 22% of SUID deaths in the United States attributed to maternal smoking. In chapters 2 and 3, the authors differentiate between two distinct subpopulations of SUID deaths, depending on the age of the infant at the time of death. In chapter 3, the authors focus on sudden unexpected postnatal collapse (SUPC), a subcategory of SUID, and found that SUPC deaths differed statistically from SUID deaths occurring from 7-364 days of age, highlighting the need for adequate nurse staffing during the immediate recovery period and postpartum stay. Chapters 4 and 5 focus on COVID-19 and the use of open data to infer causality from observational data using synthetic controls and Bayesian structural time series. Chapter 4 analyzes the relationship between vaccination and reducing COVID-19 deaths in Washington state, finding a 27.1% decrease in deaths compared to the synthetic control. In chapter 5, the authors analyze the association between COVID-19 public health measures and the epidemiology of infectious conjunctivitis, finding a 34% decrease in search activity and a 37% decrease in emergency department encounters for infectious conjunctivitis after the adoption of COVID-19-associated public health measures. In conclusion, the use of AI and machine learning in health has immense potential, but investments in data collection and open data are essential. The thesis discussed in the article shows how machine learning can be used to address critical health-related questions and underscores the need for continued investment in this field. AI in health is still in its early stages, and we can expect further advancements in the future.

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Maternity Nurses' Knowledge about Sudden Unexpected Postnatal Collapse and Safe Newborn Positioning

Cited by 8 publications

References 27 publications

Sudden Unexpected Postnatal Collapse Resulting in Newborn Death in the United States

Sudden Unexpected Postnatal Collapse Resulting in Newborn Death in the United States

Infant Safe Sleep Initiative in a Small Volume Maternity Service

AI methods and their application to health and prevention using open data.

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