Seasonal and depth-driven changes in rhodolith bed structure and associated macroalgae off Arvoredo island (southeastern Brazil)

Rhodoliths are formed by coralline red algae and can form heterogeneous substrata with high biodiversity. Here we describe a rhodolith bed at the southern limit of the known distribution of this habitat in the western Atlantic. We characterized rhodolith and macroalgal assemblages at 5, 10 and 15. m depth during summer and winter. Lithothamnion crispatum was dominant amongst the six rhodolith-forming species present. Most rhodoliths were spheroidal in shape indicating high mobility due to water movement. Rhodolith density decreased with increasing depth and during winter. Turf-forming seaweeds accounted for 60% of the biomass growing on rhodoliths. Macroalgae increased abundance and richness in the summer, but was similar between 5 and 15. m depth. They were less abundant and diverse than that recorded in rhodolith beds further north in Brazil. Both, season and depth, affected the structure of the macroalgae assemblages. We conclude that Lithothamniom is the most representative genus of Brazilian rhodolith beds. Summer is responsible for increasing the diversity and richness of macroalgae, as well as increasing rhodolith density. © 2013 Elsevier B.V

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study.

OBJECTIVE: To generate a global reference for caesarean section (CS) rates at health facilities. DESIGN: Cross-sectional study. SETTING: Health facilities from 43 countries. POPULATION/SAMPLE: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. METHODS: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. MAIN OUTCOME MEASURES: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. RESULTS: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). CONCLUSIONS: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. TWEETABLE ABSTRACT: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems