Increasing concern over the impact of hot weather on health has fostered the development of public health interventions to reduce heat-related health impacts. However, evidence of the effectiveness of such interventions is rarely cited for justification. Our objective was to review peer-reviewed and grey literature evaluating interventions aimed at reducing morbidity and/or mortality in populations during hot weather episodes. Among studies considering public risk perceptions, most respondents were aware when an extreme heat episode was occurring but did not necessarily change their practices, primarily due to a lack of self-perception as vulnerable and confusion about the appropriate actions to be taken. Among studies of health outcomes during and following heat episodes, studies were suggestive of positive impacts in reducing morbidity and mortality. While the limited evaluative work to date suggests a positive impact of public health interventions, concern persists about whether the most vulnerable groups, like the elderly and homeless, are being adequately reached.

Gastroschisis in Canada is associated with maternal risk factors, some of which are modifiable. Further studies into sociodemographic birth defect risk are necessary to allow targeted improvements in perinatal health service delivery and health policy.

WHAT'S KNOWN ON THIS SUBJECT: Extremely preterm infants are at high risk of neonatal mortality or morbidities. Existing prediction models focus on mortality, specific morbidities, or composite mortality and morbidity outcomes and ignore differences in outcome severity. WHAT THIS STUDY ADDS:A simple and practical statistical model was developed that can be applied on the first day after NICU admission to predict outcome severity spanning from no morbidity to mortality. The model is highly discriminative (Cstatistic = 90%) and internally valid. abstract OBJECTIVE: To develop and validate a statistical prediction model spanning the severity range of neonatal outcomes in infants born at #30 weeks' gestation. METHODS:A national cohort of infants, born at 23 to 30 weeks' gestation and admitted to level III NICUs in Canada in 2010-2011, was identified from the Canadian Neonatal Network database. A multinomial logistic regression model was developed to predict survival without morbidities, mild morbidities, severe morbidities, or mortality, using maternal, obstetric, and infant characteristics available within the first day of NICU admission. Discrimination and calibration were assessed using a concordance C-statistic and the C g goodnessof-fit test, respectively. Internal validation was performed using a bootstrap approach. RESULTS:Of 6106 eligible infants, 2280 (37%) survived without morbidities, 1964 (32%) and 1251 (21%) survived with mild and severe morbidities, respectively, and 611 (10%) died. Predictors in the model were gestational age, small (,10th percentile) for gestational age, gender, Score for Neonatal Acute Physiology version II .20, outborn status, use of antenatal corticosteroids, and receipt of surfactant and mechanical ventilation on the first day of admission. High model discrimination was confirmed by internal bootstrap validation (bias-corrected C-statistic = 0.899, 95% confidence interval = 0.894-0.903). Predicted probabilities were consistent with the observed outcomes (C g P value = .96).CONCLUSIONS: Neonatal outcomes ranging from mortality to survival without morbidity in extremely preterm infants can be predicted on their first day in the NICU by using a multinomial model with good discrimination and calibration. The prediction model requires additional external validation. Pediatrics 2013;132:e876-e885 AUTHORS: