Measuring global health inequity

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Notions of equity are fundamental to, and drive much of the current thinking about global health. Health inequity, however, is usually measured using health inequality as a proxy – implicitly conflating equity and equality. Unfortunately measures of global health inequality do not take account of the health inequity associated with the additional, and unfair, encumbrances that poor health status confers on economically deprived populations. Method: Using global health data from the World Health Organization's 14 mortality sub-regions, a measure of global health inequality (based on a decomposition of the Pietra Ratio) is contrasted with a new measure of global health inequity. The inequity measure weights the inequality data by regional economic capacity (GNP per capita). Results: The least healthy global sub-region is shown to be around four times worse off under a health inequity analysis than would be revealed under a straight health inequality analysis. In contrast the healthiest sub-region is shown to be about four times better off. The inequity of poor health experienced by poorer regions around the world is significantly worse than a simple analysis of health inequality reveals. Conclusion: By measuring the inequity and not simply the inequality, the magnitude of the disparity can be factored into future economic and health policy decision making

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

Physical Modelling of Large Area 4H-SiC PiN Diodes

The recent developments in SiC PiN diode research mean that physics-based models that allow accurate, rapid prediction of switching and conduction performance and resulting converter losses will soon be required. This is especially the case given the potential for very high voltage converters to be used for enabling distributed and renewable power generation. In this work an electro-thermal compact model of a 4.5 kV silicon carbide PiN diode has been developed for converter loss modelling in Simulink. Good matching of reverse recovery has been achieved between 25 and 200 °C. The I-V characteristics of the P+ anode contact have been shown to be significant in obtaining good matching for the forward characteristics of the diode, requiring further modelling work in this area. © 2009 IEEE

Where is information quality lost at clinical level? A mixed-method study on information systems and data quality in three urban Kenyan ANC clinics

Background: Well-working health information systems are considered vital with the quality of health data ranked of highest importance for decision making at patient care and policy levels. In particular, health facilities play an important role, since they are not only the entry point for the national health information system but also use health data (and primarily) for patient care. Design: A multiple case study was carried out between March and August 2012 at the antenatal care (ANC) clinics of two private and one public Kenyan hospital to describe clinical information systems and assess the quality of information. The following methods were developed and employed in an iterative process: workplace walkthroughs, structured and in-depth interviews with staff members, and a quantitative assessment of data quality (completeness and accurate transmission of clinical information and reports in ANC). Views of staff and management on the quality of employed information systems, data quality, and influencing factors were captured qualitatively. Results: Staff rated the quality of information higher in the private hospitals employing computers than in the public hospital which relies on paper forms. Several potential threats to data quality were reported. Limitations in data quality were common at all study sites including wrong test results, missing registers, and inconsistencies in reports. Feedback was seldom on content or quality of reports and usage of data beyond individual patient care was low. Conclusions: We argue that the limited data quality has to be seen in the broader perspective of the information systems in which it is produced and used. The combination of different methods has proven to be useful for this. To improve the effectiveness and capabilities of these systems, combined measures are needed which include technical and organizational aspects (e.g. regular feedback to health workers) and individual skills and motivation

Human rights education in patient care

Abstract This article explores how human rights education in the health professions can build knowledge, change culture, and empower advocacy. Through a study of educational initiatives in the field, the article analyzes different methods by which health professionals come to see the relevance of human rights norms for their work, to habituate these norms in everyday practice, and to espouse these norms in advocacy for social justice. The article seeks to show the transformative potential of education for human rights in patient care