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Objective The objective of this study was to examine the association of smoking with Primary Sjögren syndrome (pSS) classification and pSS diagnostic test results. We hypothesized that past and current smokers would have lower odds of being classified as having Sjögren syndrome (SS) and lower odds of having abnormal individual SS diagnostic test results compared with nonsmokers. Methods Participants with suspected or established pSS were enrolled into the Sjögren's International Collaborative Clinical Alliance (SICCA) registry and had oral, ocular, and rheumatologic examinations performed; blood and saliva samples collected; and labial salivary gland biopsy examinations performed; they also completed questionnaires at baseline. Logistic regression was used to determine whether smoking status was associated with pSS classification and individual pSS diagnostic test results. Results A total of 3514 participants were enrolled in SICCA. A total of 1541 (52.9%) met classification criteria for pSS. Compared with never smokers, current smokers had reduced odds of being classified as having pSS, reduced odds of having a focus score ≥ 1 and serologic positivity for anti‐SSA/anti‐SSB antibodies, and lower odds of having abnormal signs or test results of dry eye disease. Compared with never smokers, past smokers did not have a statistically significant reduction in odds of being classified as having pSS and of having abnormal individual pSS diagnostic test results. Conclusion Compared with never smokers, current smokers in the SICCA cohort had lower odds of being classified as having pSS, lower odds of exhibiting abnormal signs and test results for dry eye disease, and lower odds of having a labial salivary gland biopsy supportive of pSS classification. Such negative associations, however, do not suggest that current smoking is of any benefit with respect to pSS.

Abstract Background and aims The American Academy of Pediatrics describes late preterm infants, born at 34 to 36 completed weeks' gestation, as at‐risk for rehospitalization and severe morbidity as compared to term infants. While there are prediction models that focus on specific morbidities, there is limited research on risk prediction for early readmission in late preterm infants. The aim of this study is to derive and validate a model to predict 7‐day readmission. Methods This is a population‐based retrospective cohort study of liveborn infants in California between January 2007 to December 2011. Birth certificates, maintained by California Vital Statistics, were linked to a hospital discharge, emergency department, and ambulatory surgery records maintained by the California Office of Statewide Health Planning and Development. Random forest and logistic regression were used to identify maternal and infant variables of importance, test for association, and develop and validate a predictive model. The predictive model was evaluated for discrimination and calibration. Results We restricted the sample to healthy late preterm infants (n = 122,014), of which 4.1% were readmitted to hospital within 7‐day after birth discharge. The random forest model with 24 variables had better predictive ability than the 8 variable logistic model with c‐statistic of 0.644 (95% confidence interval 0.629, 0.659) in the validation data set and Brier score of 0.0408. The eight predictors of importance length of stay, delivery method, parity, gestational age, birthweight, race/ethnicity, phototherapy at birth hospitalization, and pre‐existing or gestational diabetes were used to drive individual risk scores. The risk stratification had the ability to identify an estimated 19% of infants at greatest risk of readmission. Conclusions Our 7‐day readmission predictive model had moderate performance in differentiating at risk late preterm infants. Future studies might benefit from inclusion of more variables and focus on hospital practices that minimize risk.

Abstract Background While racial/ethnic survival disparities have been described in pediatric oncology, the impact of income has not been extensively explored. We analyzed how public insurance influences 5‐year overall survival (OS) in young patients with sarcomas. Methods The University of California San Francisco Cancer Registry was used to identify patients aged 0‐39 diagnosed with bone or soft tissue sarcomas between 2000 and 2015. Low‐income patients were defined as those with no insurance or Medicaid, a means‐tested form of public insurance. Survival curves were computed using the Kaplan‐Meier method and compared using log‐rank tests and Cox models. Causal mediation was used to assess whether the association between public insurance and mortality is mediated by metastatic disease. Results Of 1106 patients, 39% patients were classified as low‐income. Low‐income patients were more likely to be racial/ethnic minorities and to present with metastatic disease (OR 1.96, 95% CI 1.35‐2.86). Low‐income patients had significantly worse OS (61% vs 71%). Age at diagnosis and extent of disease at diagnosis were also independent predictors of OS. When stratified by extent of disease, low‐income patients consistently had significantly worse OS (localized: 78% vs 84%, regional: 64% vs 73%, metastatic: 23% vs 30%, respectively). Mediation analysis indicated that metastatic disease at diagnosis mediated 15% of the effect of public insurance on OS. Conclusions Low‐income patients with bone and soft tissue sarcomas had decreased OS regardless of disease stage at presentation. The mechanism by which insurance status impacts survival requires additional investigation, but may be through reduced access to care.

Background Infants with critical congenital heart disease (CCHD) are more likely to be small for gestational age (SGA) or born to mothers with maternal placental syndrome. The objective of this study was to investigate the relationship between maternal placental syndrome, SGA, and gestational age (GA) on 1‐year mortality in infants with CCHD. Methods and Results In a population‐based administrative database of all live‐born infants in California (2007–2012) we identified all infants with CCHD without chromosomal anomalies. Our primary predictor was an impaired fetal environment (IFE), defined as presence of maternal placental syndrome or SGA. We calculated hazard ratios to quantify the association between different components of IFE and 1‐year mortality and conducted a causal mediation analysis to assess GA at birth as a mediator. We identified 6863 infants with CCHD. IFE was present in 25.1%. Infants with IFE were more likely to die than infants without IFE (16.6% versus 11.1%; hazard ratios 1.55, 95% CI 1.34–1.78). Only SGA (hazard ratios 1.76, 95% CI 1.50–2.05) and placental abruption (hazard ratios 1.70, 95% CI 1.17–2.48) were significantly associated with mortality; preeclampsia and gestational hypertension had no significant association with mortality. The mediation analysis showed that 32.8% (95% CI 24.9–47.0%) of the relationship between IFE and mortality is mediated through GA. Conclusions IFE is a significant contributor to outcomes in the CCHD population. SGA and placental abruption are the main drivers of postnatal mortality while other maternal placental syndrome components had much less of an impact. Only one third of the effect between IFE and mortality is mediated through GA.