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In the United States (US), neonatal intensive care units (NICUs) monitor and treat newborns for a variety of adverse health concerns including preterm status, respiratory distress and restricted growth. As such, NICU admission is an integrated measure of neonatal risk. We linked 2018 US national birth registry NICU admission data among singleton births with satellite and modelled air pollution levels for the month prior to birth to examine whether late-pregnancy exposure to ambient air pollutants is associated with adverse neonatal health outcomes. Regardless of season, higher ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter < 2.5 microns (PM 2.5 ) increased the likelihood of NICU admission 30-35% for NO 2 and 11-22% for PM 2.5 even after adjustment for parental characteristics. Results for ozone exposure were inconsistent with largely null or reduced risk except for summer months. Despite the relatively low-moderate US exposure levels, traffic-related pollutants near the end of pregnancy appear to increase overall adverse health risks for newborns, underscoring the need to reduce prenatal exposure to ambient pollutants., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Increased placental vascular resistance is a proposed mechanism by which air pollution exposure during pregnancy lowers birth weight and increases pregnancy-induced hypertensive disorders., Objective: To examine the impact of acute air pollution exposure during pregnancy on uterine and umbilical artery Doppler indicators of placental vascular resistance., Methods: After a first ultrasound to confirm gestational age, 2562 pregnant women recruited in 12 clinics throughout the United States underwent up to five standardized ultrasounds with Doppler measurements. Exposures to 11 air pollutants were estimated for the hour of ultrasound and each of the 2 h prior to ultrasound at the clinics using the National Air Quality Forecast Capability reanalysis products. We used mixed logistic regression to study the longitudinal odds ratio (OR) of any, uni- or bi-lateral systolic and diastolic uterine artery notching compared to no notching and the longitudinal OR of abnormal end diastolic flow of the umbilical artery compared to forward flow. Uterine and umbilical artery resistance indexes were studied using linear mixed models., Results: Each inter-quartile range (IQR) increase of particulate matter < 2.5 μm, nitrate, ammonium, primary organic matter (POM) and nitrogen dioxide during the hour of ultrasound was associated with a decreased risk of unilateral systolic notch and with increased resistance index of the left uterine artery. For the umbilical artery, each IQR increase in ozone was associated with decreased resistance index (b: -0.26, 95 % CI: -0.52, -0.01) and with a decreased risk of abnormal end diastolic flow (OR: 0.36, 95 % CI: 0.14, 0.94); while each IQR increase of elemental carbon and POM was associated with increased risk of abnormal end diastolic flow (OR: 1.47, 95 % CI: 1.02, 2.13 and OR: 1.67, 95 % CI: 1.17, 2.39, respectively)., Discussion: Our results suggest acute air pollution exposure may influence placental vascular resistance., (Published by Elsevier Inc.)

Introduction: Appropriate stratification of injury severity is a critical tool in the assessment of the treatment and the prevention of injury. Since its inception, the Injury Severity Score (ISS) has been the generally recognized "gold standard" for anatomic injury severity assessment. However, there is considerable time and expense involved in the collection of the information required to calculate an accurate ISS. In addition, the predictive power of the ISS has been shown to be limited. Previous work has demonstrated that the anatomic information about injury contained in the International Classification of Diseases Version 9 (ICD-9) can be a significant predictor of survival in trauma patients. The goal of this study was to utilize the San Diego County Trauma Registry (SDTR), one of the nation's leading trauma registries, to compare the predictive power of the ISS with the predictive power of the information contained in the injured patients' ICD-9 diagnoses codes. It was our primary hypothesis that survival risk ratios derived from patients' ICD-9 diagnoses codes would be equal or better predictors of survival than the Injury Severity Score. The implications of such a finding would have the potential for significant cost savings in the care of injured patients., Methods: Data for the test population were obtained from the SDTR, which contains data from 1985 through 1993 from five participating hospitals. Four data sources were utilized to estimate the expected survival rate/mortality rate for each ICD-9 code in the SDTR. These were (1) the SDTR patients themselves, (2) the North Carolina State Hospital Discharge Database, (3) the North Carolina Trauma Registry Database, and (4) the Agency for Health Care Policy Research's Health Care Utilization Project Database. Each of these data sources was separately utilized to develop a survival risk ratio (SRR) for each ICD-9 diagnoses code. The SRR was calculated by dividing the number of survivors for patients with each ICD-9 code by the total number of all patients with the particular ICD-9 diagnoses code. The four groups of SRRs derived from our four data sources were used as predictors of survival and the ability of the SRRs to predict survival was compared with the predictive power of the ISS using measures of accuracy, sensitivity, specificity, and receiver operator characteristic curves., Results: During the years 1985 through 1993, complete data were available for analysis on 44,032 patients. Of these, 2,848 patients died during their hospitalization (6%). Survival risk ratios were calculated for each of the diagnoses in the data base. Logistic regression, using the SAS System for statistical analysis, was used to assess the relative predictive power of the ISS and the survival risk ratios derived from the ICD-9 diagnoses codes from each of the four data bases. The analyses demonstrated that the regression models using the SRRs were generally as good or better than ISS as predictors of survival. The predictive power of the SRRs derived from the SDTR data, the North Carolina Trauma Registry data and the Health Care Utilization Report data were the best. In a subsequent analysis, the SRR values and the ISS were added to the patient's age and the revised Trauma Scores to create new predictive models in the mode of TRISS methodology. The analyses again indicated that the models using SRRs had as good or better predictive power than the model using the ISS., Conclusions: The present study confirms previous work showing that survival risk ratios derived from injured patients' ICD-9 diagnoses codes are as good as or better than ISS as predictors of survival.