OBJECTIVES: Maternal smoking during pregnancy is an established risk factor for sudden unexpected infant death (SUID). Here, we aim to investigate the effects of maternal prepregnancy smoking, reduction during pregnancy, and smoking during pregnancy on SUID rates. METHODS: We analyzed the Centers for Disease Control and Prevention Birth Cohort Linked Birth/Infant Death Data Set (2007-2011: 20 685 463 births and 19 127 SUIDs). SUID was defined as deaths at ,1 year of age with International Classification of Diseases, 10th Revision codes R95 (sudden infant death syndrome), R99 (ill-defined or unknown cause), or W75 (accidental suffocation or strangulation in bed). RESULTS: SUID risk more than doubled (adjusted odds ratio [aOR] = 2.44; 95% confidence interval [CI] 2.31-2.57) with any maternal smoking during pregnancy and increased twofold between no smoking and smoking 1 cigarette daily throughout pregnancy. For 1 to 20 cigarettes per day, the probability of SUID increased linearly, with each additional cigarette smoked per day increasing the odds by 0.07 from 1 to 20 cigarettes; beyond 20 cigarettes, the relationship plateaued. Mothers who quit or reduced their smoking decreased their odds compared with those who continued smoking (reduced: aOR = 0.88, 95% CI 0.79-0.98; quit: aOR = 0.77, 95% CI 0.67-0.87). If we assume causality, 22% of SUIDs in the United States can be directly attributed to maternal smoking during pregnancy. CONCLUSIONS: These data support the need for smoking cessation before pregnancy. If no women smoked in pregnancy, SUID rates in the United States could be reduced substantially.

OBJECTIVES: In most recent studies, authors combine all cases of sudden infant death syndrome, other deaths from ill-defined or unknown causes, and accidental suffocation and strangulation in bed as a single population to analyze sudden unexpected infant death (SUID). Our aim with this study is to determine if there are statistically different subcategories of SUID that are based on the age of death of an infant. METHODS: In this retrospective, cross-sectional analysis, 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). Logistic regression models were developed to identify subpopulations of SUID cases by age of death, and we subsequently analyzed the effects of a set of covariates on each group. RESULTS: Two groups were identified: sudden unexpected early neonatal deaths (SUENDs; days 0-6) and postperinatal SUIDs (days 7-364). These groups significantly differed in the distributions of assigned International Classification of Diseases, 10th Revision code, live birth order, marital status, age of mother, birth weight, and gestational length compared to postperinatal SUIDs (days 7-364). Maternal smoking during pregnancy was not a significant risk factor for deaths that occurred in the first 48 hours. CONCLUSIONS: SUEND should be considered as a discrete entity from postperinatal SUID in future studies. These data could help improve the epidemiological understanding of SUEND and SUID and provide clues to a mechanistic understanding underlying the causes of death.

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics -- a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients’ clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

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IMPORTANCE Infectious conjunctivitis is highly transmissible and a public health concern. While mitigation strategies have been successful on a local level, population-wide decreases in spread are rare.OBJECTIVE To evaluate whether internet search interest and emergency department visits for infectious conjunctivitis were associated with public health interventions adopted during the COVID-19 pandemic.DESIGN, SETTING, AND PARTICIPANTS Internet search data from the US and emergency department data from a single academic center in the US were used in this study. Publicly available smartphone mobility data were temporally aligned to quantify social distancing. Internet search term trends for nonallergic conjunctivitis, corneal abrasions, and posterior vitreous detachments were obtained. Additionally, all patients who presented to a single emergency department from February 2015 to February 2021 were included in a review. Physician notes for emergency department visits at a single academic center with the same diagnoses were extracted. Causal inference was performed using a bayesian structural time-series model. Data were compared from before and after April 2020, when the US Centers for Disease Control and Prevention recommended members of the public wear masks, stay at least 6 feet from others who did not reside in the same home, avoid crowds, and quarantine if experiencing flulike symptoms or exposure to persons with COVID-19 symptoms.EXPOSURES Symptoms of or interest in conjunctivitis in the context of the COVID-19 pandemic. MAIN OUTCOME AND MEASURESThe hypothesis was that there would be a decrease in internet search interest and emergency department visits for infectious conjunctivitis after the adaptation of public health measures targeted to curb COVID-19.RESULTS A total of 1156 emergency department encounters with a diagnosis of conjunctivitis were noted from January 2015 to February 2021. Emergency department encounters for nonallergic conjunctivitis decreased by 37.3% (95% CI, −12.9% to −60.6%; P < .001). In contrast, encounters for corneal abrasion (1.1% [95% CI, −29.3% to 29.1%]; P = .47) and posterior vitreous detachments (7.9% [95% CI, −46.9% to 66.6%]; P = .39) remained stable after adjusting for total emergency department encounters. Search interest in conjunctivitis decreased by 34.2% (95% CI, −30.6% to −37.6%; P < .001) after widespread implementation of public health interventions to mitigate COVID-19.CONCLUSIONS AND RELEVANCE Public health interventions, such as social distancing, increased emphasis on hygiene, and travel restrictions during the COVID-19 pandemic, were associated with decreased search interest in nonallergic conjunctivitis and conjunctivitisassociated emergency department encounters. Mobility data may provide novel metrics of social distancing. These data provide evidence of a sustained population-wide decrease in infectious conjunctivitis.

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