Analysis of 137 obstetric fistula cases seen at three fistula centres in northwest Nigeria

Objectives: To determine the contributory factors to development of obstetric fistula and to determine the knowledge and practice of modern contraception among fistula patients.Design: Descriptive hospital cross-sectional study.Setting: Three fistula centres in north west Nigeria.Subjects: All obstetric fistula patients that met the inclusion criteria.Results: Of 137 cases of obstetric fistula patients that satisfied the inclusion criteria, 88% had only Vesico-vaginal fistula, while 10% and 2% had recto-vaginal fistula, and combined vesico vaginal fistula and recto-vaginal fistula respectively. All the patients had early marriage (before age 20 years) with median ages at first marriage of 15 years and at presentation in hospital of 16 years. Majority (93.4%) of the patients developed fistula during the first delivery. Approximately two-third of the patients had no form of education. Only 42.3% of the patients received antenatal care and 86% delivered at home. Only 28% of the patients was aware of modern contraception and 2% had used modern contraceptive before developing fistula. All the patients expressed willingness to use modern contraception after fistula repair.Conclusion: This study shows that child marriage, low education, unskilled birth attendance and low contraceptive uptake are common among the obstetric fistula patients in north west Nigeria. Public advocacy and formulation of laws and policies to protect girls from early marriage, girl child education to secondary school level be encouraged, public education on the importance of and utilisation of maternity and modern family planning services

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

State of the art survey of deep learning and machine learning models for smart cities and urban sustainability

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems