Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst.

BACKGROUND/OBJECTIVES: Pancreatic cyst management can be distilled into three separate pathways - discharge, monitoring or surgery- based on the risk of malignant transformation. This study compares the performance of artificial intelligence (AI) models to clinical care for this task. METHODS: Two explainable boosting machine (EBM) models were developed and evaluated using clinical features only, or clinical features and cyst fluid molecular markers (CFMM) using a publicly available dataset, consisting of 850 cases (median age 64; 65 % female) with independent training (429 cases) and holdout test cohorts (421 cases). There were 137 cysts with no malignant potential, 114 malignant cysts, and 599 IPMNs and MCNs. RESULTS: The EBM and EBM with CFMM models had higher accuracy for identifying patients requiring monitoring (0.88 and 0.82) and surgery (0.66 and 0.82) respectively compared with current clinical care (0.62 and 0.58). For discharge, the EBM with CFMM model had a higher accuracy (0.91) than either the EBM model (0.84) or current clinical care (0.86). In the cohort of patients who underwent surgical resection, use of the EBM-CFMM model would have decreased the number of unnecessary surgeries by 59 % (n = 92), increased correct surgeries by 7.5 % (n = 11), identified patients who require monitoring by 122 % (n = 76), and increased the number of patients correctly classified for discharge by 138 % (n = 18) compared to clinical care. CONCLUSIONS: EBM models had greater sensitivity and specificity for identifying the correct management compared with either clinical management or previous AI models. The model predictions are demonstrated to be interpretable by clinicians.

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India.

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

Geographic Variation in Sudden Unexpected Infant Death in the United States.

OBJECTIVES: To assess the geographic variation of sudden unexpected infant death (SUID) and test if variation in geographic factors, such as state, latitude, and longitude, play a role in SUID risk across the US. STUDY DESIGN: We analyzed the Centers for Disease Control and Prevention's Cohort Linked Birth/Infant Death dataset (2005-2010; 22 882 SUID cases, 25 305 837 live births, rate 0.90/1000). SUID was defined as infant deaths (ages 7-364 days) that included sudden infant death syndrome, ill-defined and unknown cause of mortality, and accidental suffocation and strangulation in bed. SUID geographic variation was analyzed using 2 statistical models, logistic regression and generalized additive model (GAM). RESULTS: Both models produced similar results. Without adjustment, there was marked geographic variation in SUID rates, but the variation decreased after adjusting for covariates including known risk factors for SUID. After adjustment, nine states demonstrated significantly higher or lower SUID mortality than the national average. Geographic contribution to SUID risk in terms of latitude and longitude were also attenuated after adjustment for covariates. CONCLUSION: Understanding why some states have lower SUID rates may enhance SUID prevention strategies.

Improved estimation of the relationship between fetal growth and late stillbirth in the United States, 2014-15.

In the United States the rate of stillbirth after 28 weeks' gestation (late stillbirth) is 2.7/1000 births. Fetuses that are small for gestational age (SGA) or large for gestational age (LGA) are at increased risk of stillbirth. SGA and LGA are often categorized as growth or birthweight ≤ 10th and ≥ 90th centile, respectively; however, these cut-offs are arbitrary. We sought to characterize the relationship between birthweight and stillbirth risk in greater detail. Data on singleton births between 28- and 44-weeks' gestation from 2014 to 2015 were extracted from the US Centers for Disease Control and Prevention live birth and fetal death files. Growth was assessed using customized birthweight centiles (Gestation Related Optimal Weight; GROW). The analyses included logistic regression using SGA/LGA categories and a generalized additive model (GAM) using birthweight centile as a continuous exposure. Although the SGA and LGA categories identified infants at risk of stillbirth, categorical models provided poor fits to the data within the high-risk bins, and in particular markedly underestimated the risk for the extreme centiles. For example, for fetuses in the lowest GROW centile, the observed rate was 39.8/1000 births compared with a predicted rate of 11.7/1000 from the category-based analysis. In contrast, the model-predicted risk from the GAM tracked closely with the observed risk, with the GAM providing an accurate characterization of stillbirth risk across the entire birthweight continuum. This study provides stillbirth risk estimates for each GROW centile, which clinicians can use in conjunction with other clinical details to guide obstetric management.

Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial.

BACKGROUND: Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot. OBJECTIVE: We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot. METHODS: The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat's persona ("personality"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs. RESULTS: We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute's SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants' frustration with QuitBot's inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft's Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs. CONCLUSIONS: The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231.

Maternal Obesity and Risk of Sudden Unexpected Infant Death.

Importance: Rates of maternal obesity are increasing in the US. Although obesity is a well-documented risk factor for numerous poor pregnancy outcomes, it is not currently a recognized risk factor for sudden unexpected infant death (SUID). Objective: To determine whether maternal obesity is a risk factor for SUID and the proportion of SUID cases attributable to maternal obesity. Design, Setting, and Participants: This was a US nationwide cohort study using Centers for Disease Control and Prevention National Center for Health Statistics linked birth-infant death records for birth cohorts in 2015 through 2019. All US live births for the study years occurring at 28 weeks' gestation or later from complete reporting areas were eligible; SUID cases were deaths occurring at 7 to 364 days after birth with International Statistical Classification of Diseases, Tenth Revision cause of death code R95 (sudden infant death syndrome), R99 (ill-defined and unknown causes), or W75 (accidental suffocation and strangulation in bed). Data were analyzed from October 1 through November 15, 2023. Exposure: Maternal prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared). Main Outcome and Measure: SUID. Results: Of 18 857 694 live births eligible for analysis (median [IQR] age: maternal, 29 [9] years; paternal, 31 [9] years; gestational, 39 [2] weeks), 16 545 died of SUID (SUID rate, 0.88/1000 live births). After confounder adjustment, compared with mothers with normal BMI (BMI 18.5-24.9), infants born to mothers with obesity had a higher SUID risk that increased with increasing obesity severity. Infants of mothers with class I obesity (BMI 30.0-34.9) were at increased SUID risk (adjusted odds ratio [aOR], 1.10; 95% CI, 1.05-1.16); with class II obesity (BMI 35.0-39.9), a higher risk (aOR, 1.20; 95% CI, 1.13-1.27); and class III obesity (BMI ≥40.0), an even higher risk (aOR, 1.39; 95% CI, 1.31-1.47). A generalized additive model showed that increased BMI was monotonically associated with increased SUID risk, with an acceleration of risk for BMIs greater than approximately 25 to 30. Approximately 5.4% of SUID cases were attributable to maternal obesity. Conclusions and Relevance: The findings suggest that infants born to mothers with obesity are at increased risk of SUID, with a dose-dependent association between increasing maternal BMI and SUID risk. Maternal obesity should be added to the list of known risk factors for SUID. With maternal obesity rates increasing, research should identify potential causal mechanisms for this association.

Risk factors for late (28+ weeks' gestation) stillbirth in the United States, 2014-2015.

BACKGROUND: In the United States (US) late stillbirth (at 28 weeks or more of gestation) occurs in 3/1000 births. AIM: We examined risk factors for late stillbirth with the specific goal of identifying modifiable factors that contribute substantially to stillbirth burden. SETTING: All singleton births in the US for 2014-2015. METHODS: We used a retrospective population-based design to assess the effects of multiple factors on the risk of late stillbirth in the US. Data were drawn from the US Centers for Disease Control and Prevention live birth and fetal death data files. RESULTS: There were 6,732,157 live and 18,334 stillbirths available for analysis (late stillbirth rate = 2.72/1000 births). The importance of sociodemographic determinants was shown by higher risks for Black and Native Hawaiian and Other Pacific Islander mothers compared with White mothers, mothers with low educational attainment, and older mothers. Among modifiable risk factors, delayed/absent prenatal care, diabetes, hypertension, and maternal smoking were associated with increased risk, though they accounted for only 3-6% of stillbirths each. Two factors accounted for the largest proportion of late stillbirths: high maternal body mass index (BMI; 15%) and infants who were small for gestational age (38%). Participation in the supplemental nutrition for women, infants and children program was associated with a 28% reduction in overall stillbirth burden. CONCLUSIONS: This study provides population-based evidence for stillbirth risk in the US. A high proportion of late stillbirths was associated with high maternal BMI and small for gestational age, whereas participation in supplemental nutrition programs was associated with a large reduction in stillbirth burden. Addressing obesity and fetal growth restriction, as well as broadening participation in nutritional supplementation programs could reduce late stillbirths.

Maternal infections in pregnancy and the risk of sudden unexpected infant death in the offspring in the U.S., 2011-2015.

BACKGROUND: Infection is thought to play a part in some infant deaths. Maternal infection in pregnancy has focused on chlamydia with some reports suggesting an association with sudden unexpected infant death (SUID). OBJECTIVES: We hypothesized that maternal infections in pregnancy are associated with subsequent SUID in their offspring. SETTING: All births in the United States, 2011-2015. DATA SOURCE: Centers for Disease Control and Prevention (CDC) Birth Cohort Linked Birth-Infant Death Data Files. STUDY DESIGN: Cohort study, although the data were analysed as a case control study. Cases were infants that died from SUID. Controls were randomly sampled infants that survived their first year of life; approximately 10 controls per SUID case. EXPOSURES: Chlamydia, gonorrhea and hepatitis C. RESULTS: There were 19,849,690 live births in the U.S. for the period 2011-2015. There were 37,143 infant deaths of which 17,398 were classified as SUID cases (a rate of 0.86/1000 live births). The proportion of the control mothers with chlamydia was 1.7%, gonorrhea 0.2% and hepatitis C was 0.3%. Chlamydia was present in 3.8% of mothers whose infants subsequently died of SUID compared with 1.7% of controls (unadjusted OR = 2.35, 95% CI = 2.15, 2.56; adjusted OR = 1.08, 95% CI = 0.98, 1.19). Gonorrhea was present in 0.7% of mothers of SUID cases compared with 0.2% of mothers of controls (OR = 3.09, (2.50, 3.79); aOR = 1.20(0.95, 1.49)) and hepatitis C was present in 1.3% of mothers of SUID cases compared with 0.3% of mothers of controls (OR = 4.69 (3.97, 5.52): aOR = 1.80 (1.50, 2.15)). CONCLUSIONS: The marked attenuation of SUID risk after adjustment for a wide variety of socioeconomic and demographic factors suggests the small increase in the risk of SUID of the offspring of mothers with infection with hepatitis C in pregnancy is due to residual confounding.

Known pathogenic gene variants and new candidates detected in Sudden Unexpected Infant Death using Whole Genome Sequencing.

Purpose: To gain insights into potential genetic factors contributing to the infant's vulnerability to Sudden Unexpected Infant Death (SUID). Methods: Whole Genome Sequencing (WGS) was performed on 145 infants that succumbed to SUID, and 576 healthy adults. Variants were filtered by gnomAD allele frequencies and predictions of functional consequences. Results: Variants of interest were identified in 86 genes, 63.4% of our cohort. Seventy-one of these have been previously associated with SIDS/SUID/SUDP. Forty-three can be characterized as cardiac genes and are related to cardiomyopathies, arrhythmias, and other conditions. Variants in 22 genes were associated with neurologic functions. Variants were also found in 13 genes reported to be pathogenic for various systemic disorders. Variants in eight genes are implicated in the response to hypoxia and the regulation of reactive oxygen species (ROS) and have not been previously described in SIDS/SUID/SUDP. Seventy-two infants met the triple risk hypothesis criteria (Figure 1). Conclusion: Our study confirms and further expands the list of genetic variants associated with SUID. The abundance of genes associated with heart disease and the discovery of variants associated with the redox metabolism have important mechanistic implications for the pathophysiology of SUID.

Factors Associated With the Intention to Receive the COVID-19 Vaccine: Cross-sectional National Study.

BACKGROUND: The COVID-19 pandemic is an unprecedented public health crisis, and vaccines are the most effective means of preventing severe consequences of this disease. Hesitancy regarding vaccines persists among adults in the United States, despite overwhelming scientific evidence of safety and efficacy. OBJECTIVE: The purpose of this study was to use the Health Belief Model (HBM) and reasoned action approach (RAA) to examine COVID-19 vaccine hesitancy by comparing those who had already received 1 vaccine to those who had received none. METHODS: This study examined demographic and theory-based factors associated with vaccine uptake and intention among 1643 adults in the United States who completed an online survey during February and March 2021. Survey items included demographic variables (eg, age, sex, political ideology), attitudes, and health belief variables (eg, perceived self-efficacy, perceived susceptibility). Hierarchical logistic regression analyses were used for vaccine uptake/intent. The first model included demographic variables. The second model added theory-based factors to examine the association of health beliefs and vaccine uptake above and beyond the associations explained by demographic characteristics alone. RESULTS: The majority of participants were male (n=974, 59.3%), White (n=1347, 82.0%), and non-Hispanic (n=1518, 92.4%) and reported they had already received a COVID-19 vaccine or definitely would when it was available to them (n=1306, 79.5%). Demographic variables significantly associated with vaccine uptake/intent included age (adjusted odds ratio [AOR] 1.05, 95% CI 1.04-1.06), other race (AOR 0.47, 95% CI 0.27-0.83 vs White), and political ideology (AOR 15.77, 95% CI 7.03-35.35 very liberal vs very conservative). The theory-based factors most strongly associated with uptake/intention were attitudes (AOR 3.72, 95% CI 2.42-5.73), self-efficacy (AOR 1.75, 95% CI 1.34-2.29), and concerns about side effects (AOR 0.59, 95% CI 0.46-0.76). Although race and political ideology were significant in the model of demographic characteristics, they were not significant when controlling for attitudes and beliefs. CONCLUSIONS: Vaccination represents one of the best tools to combat the COVID-19 pandemic, as well as other possible pandemics in the future. This study showed that older age, attitudes, injunctive norms, descriptive norms, and self-efficacy are positively associated with vaccine uptake and intent, whereas perceived side effects and lack of trust in the vaccine are associated with lower uptake and intent. Race and political ideology were not significant predictors when attitudes and beliefs were considered. Before vaccine hesitancy can be addressed, researchers and clinicians must understand the basis of vaccine hesitancy and which populations may show higher hesitancy to the vaccination so that interventions can be adequately targeted.

Association of Public Health Measures During the COVID-19 Pandemic With the Incidence of Infectious Conjunctivitis.

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 Measures: The 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 conjunctivitis-associated 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.

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

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 in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes 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.

Survey data and human computation for improved flu tracking.

While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users' online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 -human interactome.

Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a's interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2.

Distinct Populations of Sudden Unexpected Infant Death Based on Age.

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.

Maternal Smoking Before and During Pregnancy and the Risk of Sudden Unexpected Infant Death.

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.