Impact of integrated community-facility interventions model on neonatal mortality in rural Bangladesh- a quasi-experimental study

Background Neonatal mortality remains unacceptably high in many countries. WHO recommends that all newborns be assessed during the postnatal period and should seek prompt medical care if there is any danger sign. However, in many developing countries, only a small proportion of women receive postnatal care. Also, the quality of care in public health facilities is suboptimal. Methods We designed an intervention package that included community health worker-assisted pregnancy and birth surveillance, post-natal visits to assess newborns on the first, third, seventh and twenty-eighth days of birth, referral for facility-based care, and establishing a newborn stabilization unit at the first level referral health facility. We did a quasi-experimental, propensity-score matched, controlled study in the Sylhet region of Bangladesh. We used a cross-sectional survey method at baseline and endline to measure the effect of our intervention. We considered two indicators for the primary outcome–(a) all-cause neonatal mortality rate and (b) case fatality of severe illness. Secondary outcomes were the proportion of neonates with signs and symptoms of severe illness who sought care in a hospital or a medically qualified provider. Results Our sample size was 9,940 live births (4,257 at baseline, 5,683 at end line). Our intervention was significantly associated with a 39% reduction (aRR = 0.61, 95% CI: 0.40–0.93; p = 0.046) in the risk of neonatal mortality and 45% reduction (aRR = 0.55, 95% CI: 0.35–0.86; p = 0.001) in the risk of case fatality of severe illness among newborns in rural Bangladesh. The intervention significantly increased the care-seeking for severe illness at the first-level referral facility (DID 36.6%; 95% CI % 27.98 to 45.22; p<0.001). Interpretation Our integrated community-facility interventions model resulted in early identification of severely sick neonates, early care seeking and improved treatment. The interventions led to a significant reduction in all-cause neonatal mortality and case fatality from severe illness

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

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

Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model

10.1016/S0890-6955(02)00264-XInternational Journal of Machine Tools and Manufacture434405-419IMTM