Accessibility and utilisation of delivery care within a Skilled Care Initiative in rural Burkina Faso.

OBJECTIVES: The Skilled Care Initiative (SCI) was a comprehensive skilled attendance at delivery strategy implemented by the Ministry of Health and Family Care International in Ouargaye district (Burkina Faso) from 2002 to 2005. We aimed to evaluate the relationships between accessibility, functioning of health centres and utilisation of delivery care in the SCI intervention district (Ouargaye) and compare this with another district (Diapaga). METHODS: Data were collected on staffing, equipment, water and energy supply for all health centres and a functionality index for health centres were constructed. A household census was carried out in 2006 to assess assets of all household members, and document pregnancies lasting more than 6 months between 2001 and 2005, with place of delivery and delivery attendant. Utilisation of delivery care was defined as birth in a health institution or birth by Caesarean section. Analyses included univariate and multivariate logistic regression. RESULTS: Distance to health facility, education and asset ownership were major determinants of delivery care utilisation, but no association was found between the functioning of health centres (as measured by infrastructure, energy supply and equipment) and institutional birth rates or births by Caesarean section. The proportion of births in an institution increased more substantially in the SCI district over time but no changes were seen in Caesarean section rates. CONCLUSION: The SCI has increased uptake of institutional deliveries but there is little evidence that it has increased access to emergency obstetric care, at least in terms of uptake of Caesarean sections. Its success is contingent on large-scale coverage and 24-h availability of referral for life saving drugs, skilled personnel and surgery for pregnant women

Quantity of documentation of maltreatment risk factors in injury-related paediatric hospitalisations

<p>Abstract</p> <p>Background</p> <p>While child maltreatment is recognised as a global problem, solid epidemiological data on the prevalence of child maltreatment and risk factors associated with child maltreatment is lacking in Australia and internationally. There have been recent calls for action to improve the evidence-base capturing and describing child abuse, particularly those data captured within the health sector. This paper describes the quantity of documentation of maltreatment risk factors in injury-related paediatric hospitalisations in Queensland, Australia.</p> <p>Methods</p> <p>This study involved a retrospective medical record review, text extraction and coding methodology to assess the quantity of documentation of risk factors and the subsequent utility of data in hospital records for describing child maltreatment and data linkage to Child Protection Service (CPS).</p> <p>Results</p> <p>There were 433 children in the maltreatment group and 462 in the unintentional injury group for whom medical records could be reviewed. Almost 93% of the maltreatment code sample, but only 11% of the unintentional injury sample had documentation identified indicating the presence of any of 20 risk factors. In the maltreatment group the most commonly documented risk factor was history of abuse (41%). In those with an unintentional injury, the most commonly documented risk factor was alcohol abuse of the child or family (3%). More than 93% of the maltreatment sample also linked to a child protection record. Of concern are the 16% of those children who linked to child protection who did not have documented risk factors in the medical record.</p> <p>Conclusion</p> <p>Given the importance of the medical record as a source of information about children presenting to hospital for treatment and as a potential source of evidence for legal action the lack of documentation is of concern. The details surrounding the injury admission and consideration of any maltreatment related risk factors, both identifying their presence and ruling them out are required for each and every case. This highlights the need for additional training for clinicians to understand the importance of their documentation in child injury cases.</p

Development of a Data Fusion Framework to support the Analysis of Aviation Big Data

The Federal Aviation Administration (FAA) is primarily responsible for the advancement, safety, and regulation of civil aviation, as well as overseeing the development of the air traffic control system in the United States. As such, it is faced with tremendous amounts of data on a daily basis. This data, which comes in high volumes, in various formats, from disparate sources and at various frequencies, is used by FAA analysts and researchers to make accurate forecasts, improve the safety and operational performance of their operations, and streamline processes. However, by its very nature, aviation Big Data presents a number of challenges to analysts: it impedes their ability to get a real-time picture of the state of the system, identify trends and operational patterns, make real-time predictions, etc. As such, the overarching objective of the present effort is to support FAA through the development of a data fusion framework to support the analysis of aviation Big Data. For the purpose of this research, three datasets were considered: System-Wide Information Management (SWIM) Flight Publication Data Service (SFDPS), Traffic Flow Management System (TFMS), and Meteorological Terminal Aviation Routine (METAR). The equivalent of one day of data was retrieved from each dataset, parsed and fused. A use case was then used to illustrate how a data fusion framework could be used by FAA analysts and researchers. The use case focused on predicting the occurrence of weather-related Ground Delay Programs (GDP) at the Newark (EWR), La Guardia (LGA), and Boston Logan (BOS) International Airports. This involved developing a prediction model using the Decision Tree Machine Learning technique. Evaluation metrics such as Matthew’s Correlation Coefficient were then used to evaluate the model’s performance. It is expected that a data fusion framework, once integrated within the FAA’s Computing and Analytics Shared Services Integrated Environment (CASSIE) could be used by analysts and researchers alike to identify trends and patterns and develop efficient methods to ensure that the U.S. civil and general aviation remains the safest in the world