Incidence of Early and Late Onset Neonatal Sepsis in Suriname: A National Tertiary Hospital Birth-cohort Study.

BACKGROUND: Early onset neonatal sepsis (EONS) and late onset neonatal sepsis (LONS) are important causes of neonatal mortality and morbidity. A pressing need for reliable and detailed data of low- and middle-income countries exists. This study aimed to describe the incidence and outcome of neonatal sepsis in the only tertiary hospital of Suriname, a middle-income country in South America. METHODS: Infants born at the Academic Hospital of Paramaribo from May 2017 through December 2018 were prospectively included at birth. Perinatal data, duration of antibiotic treatment, blood culture results and mortality data were gathered. Neonatal sepsis was defined as positive blood culture with a pathogenic microorganism within the first 28 days of life. RESULTS: Of the 2190 infants included, 483 (22%) were admitted to neonatal (intensive) care. The incidence of EONS was 2.1 (95% CI: 0.9-5) per 1000 live births, with no deaths. Antibiotics for suspected EONS were administrated to 189 (8.6%) infants, of whom 155 (82%) were born prematurely. The incidence of LONS cases was 145 (95% CI: 114-176) per 1000 admissions. Gramnegative bacteria accounted for 70% (48 out of 70) of causative organisms. Seventeen deaths were directly caused by sepsis (35 per 1000 admissions). CONCLUSIONS: Findings from this tertiary center birth cohort study in a middle-income setting indicate EONS incidence and outcomes comparable to high-income settings, whereas LONS is a more prevalent and significant challenge with a predominance of gram-negative bacteria, and high mortality.

What drives the ecological quality of surface waters? A review of 11 predictive modeling tools.

What policy is needed to ensure that good-quality water is available for both people's needs and the environment? The EU Water Framework Directive (WFD), which came into force in 2000, established a framework for the assessment, management, protection and improvement of the status of water bodies across the European Union. However, recent reviews show that the ecological status of the majority of surface waters in the EU does not meet the requirement of good status. Thus, it is an important question what measures water management authorities should take to improve the ecological status of their water bodies. To find concrete answers, several institutes in the Netherlands cooperated to develop a software tool, the WFD Explorer, to assist water managers in selecting efficient measures. This article deals with the development of prediction tools that allow one to calculate the effect of restoration and mitigation measures on the biological quality, expressed in terms of Ecological Quality Ratios (EQRs). To find the ideal modeling tool we give a review of 11 predictive models: 10 models from the field of Machine Learning and, additionally, the Multiple Regression model. We present our results in terms of a 'prediction-interpretation competition'. All these models were tested in a multiple-stressor setting: the values of 15 stressors (or steering factors) are available to predict the EQR values of four biological quality elements (phytoplankton, other aquatic flora, benthic invertebrates and fish). Analyses are based on 29 data sets from various water clusters (streams, ditches, lakes, channels). All 11 models were ranked by their predictive performance and their level of model transparency. Our review shows a trade-off between these two aspects. Models that have the best EQR prediction performance show non-transparent model structures. These are Random Forest and Boosting. However, models with low prediction accuracies show transparent response relationships between EQRs on the one hand and individual steering factors on the other hand. These models are Multiple Regression, Regression Trees and Product Unit Neural Networks. To acknowledge both aspects of model quality - predictive power and transparency - we recommend that models from both groups are implemented in the WFD Explorer software.