External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

Food Price Shocks and the Political Economy of Global Agricultural and Development Policy

The recent spikes of global food prices induced a rapid increase in mass media coverage, public policy attention, and donor funding for food security and for agriculture and rural poverty. This has occurred while the shift from low to high food prices has induced a shift in (demographic or social) location of the hunger and poverty effects, but the total number of undernourished and poor people has declined over the same period. We suggest that the observed pattern can be explained by the presence of a global urban bias on agriculture and food policy in developing countries, and we discuss whether this global urban bias may actually benefit poor farmers. We argue that the food price spikes have succeeded where others have failed in the past: to move the problems of poor and hungry farmers to the top of the policy agenda and to induce development and donor strategies to help them

News Droughts, News Floods, and U.S. Disaster Relief

This paper studies the influence of mass media on U.S. government response to approximately 5,000 natural disasters occurring between 1968 and 2002. These disasters took nearly 63,000 lives and affected 125 million people per year. We show that U.S. relief depends on whether the disaster occurs at the same time as other newsworthy events, such as the Olympic Games, which are obviously unrelated to need. We argue that the only plausible explanation of this is that relief decisions are driven by news coverage of disasters and that the other newsworthy material crowds out this news coverage. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.