Dead on arrival in a low-income country: results from a multicenter study in Pakistan

BACKGROUND: This study assessed the characteristics of dead on arrival (DOA) patients in Pakistan. METHODS: Data about the DOA patients were extracted from Pakistan National Emergency Department Surveillance study (Pak-NEDS). This study recruited all ED patients presenting to seven tertiary care hospitals during a four-month period between November 2010 and March 2011. This study included patients who were declared dead-on-arrival by the ED physician. RESULTS: A total of 1,557 DOA patients (7 per 1,000 visits) were included in the Pak-NEDS. Men accounted for two-thirds (64%) of DOA patients. Those aged 20-49 years accounted for about 46% of DOA patients. Nine percent (n = 72) of patients were brought by ambulance, and most patients presented at a public hospital (80%). About 11% of DOA patients had an injury. Factors significantly associated (p \u3c 0.05) with ambulance use were men (adjusted odds ratio [aOR] = 2.72), brought to a private hospital (OR = 2.74), and being injured (aOR = 1.89). Cardiopulmonary resuscitation (CPR) was performed on 6% (n = 42) of patients who received treatment. Those brought to a private hospital were more likely to receive CPR (aOR = 2.81). CONCLUSION: This study noted a higher burden of DOA patients in Pakistan compared to other resourceful settings (about 1 to 2 per 1,000 visits). A large proportion of patients belonging to productive age groups, and the low prevalence of ambulance and CPR use, indicate a need for improving the prehospital care and basic life support training in pakistan

Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis

Dead on arrival in a low-income country: results from a multicenter study in Pakistan

Abstract Background This study assessed the characteristics of dead on arrival (DOA) patients in Pakistan. Methods Data about the DOA patients were extracted from Pakistan National Emergency Department Surveillance study (Pak-NEDS). This study recruited all ED patients presenting to seven tertiary care hospitals during a four-month period between November 2010 and March 2011. This study included patients who were declared dead-on-arrival by the ED physician. Results A total of 1,557 DOA patients (7 per 1,000 visits) were included in the Pak-NEDS. Men accounted for two-thirds (64%) of DOA patients. Those aged 20-49 years accounted for about 46% of DOA patients. Nine percent (n = 72) of patients were brought by ambulance, and most patients presented at a public hospital (80%). About 11% of DOA patients had an injury. Factors significantly associated (p < 0.05) with ambulance use were men (adjusted odds ratio [aOR] = 2.72), brought to a private hospital (OR = 2.74), and being injured (aOR = 1.89). Cardiopulmonary resuscitation (CPR) was performed on 6% (n = 42) of patients who received treatment. Those brought to a private hospital were more likely to receive CPR (aOR = 2.81). Conclusion This study noted a higher burden of DOA patients in Pakistan compared to other resourceful settings (about 1 to 2 per 1,000 visits). A large proportion of patients belonging to productive age groups, and the low prevalence of ambulance and CPR use, indicate a need for improving the prehospital care and basic life support training in Pakistan

Nanostructuring Platinum Nanoparticles on Multilayered Graphene Petal Nanosheets for Electrochemical Biosensing

Hybridization of nanoscale metals and carbon nanotubes into composite nanomaterials has produced some of the best-performing sensors to date. The challenge remains to develop scalable nanofabrication methods that are amenable to the development of sensors with broad sensing ranges. A scalable nanostructured biosensor based on multilayered graphene petal nanosheets (MGPNs), Pt nanoparticles, and a biorecognition element (glucose oxidase) is presented. The combination of zero-dimensional nanoparticles on a two-dimensional support that is arrayed in the third dimension creates a sensor platform with exceptional characteristics. The versatility of the biosensor platform is demonstrated by altering biosensor performance (i.e., sensitivity, detection limit, and linear sensing range) through changing the size, density, and morphology of electrodeposited Pt nanoparticles on the MGPNs. This work enables a robust sensor design that demonstrates exceptional performance with enhanced glucose sensitivity (0.3 mu M detection limit, 0.0150 mM linear sensing range), a long stable shelf-life (\u3e1 month), and a high selectivity over electroactive, interfering species commonly found in human serum samples

The Pakistan National Emergency Department Surveillance Study (Pak-NEDS): Introducing a pilot surveillance

© 2015 Mir et al. Background: Evidence-based decision making is essential for appropriate prioritization and service provision by healthcare systems. Despite higher demands, data needs for this practice are not met in many cases in low- and middle-income countries because of underdeveloped sources, among other reasons. Emergency departments (EDs) provide an important channel for such information because of their strategic position within healthcare systems. This paper describes the design and pilot test of a national ED based surveillance system suitable for the Pakistani context. Methods: The Pakistan National Emergency Department Surveillance Study (Pak-NEDS) was pilot tested in the emergency departments of seven major tertiary healthcare centres across the country. The Aga Khan University, Karachi, served as the coordinating centre. Key stakeholders and experts from all study institutes were involved in outlining data needs, development of the study questionnaire, and identification of appropriate surveillance mechanisms such as methods for data collection, monitoring, and quality assurance procedures. The surveillance system was operational between November 2010 and March 2011. Active surveillance was done 24 hours a day by data collectors hired and trained specifically for the study. All patients presenting to the study EDs were eligible participants. Over 270,000 cases were registered in the surveillance system over a period of four months. Coverage levels in the final month ranged from 91-100% and were highest in centres with the least volume of patients. Overall the coverage for the four months was 79% and crude operational costs were less than $0.20 per patient. Conclusions: Pak-NEDS is the first multi-centre ED based surveillance system successfully piloted in a sample of major EDs having some of the highest patient volumes in Pakistan. Despite the challenges identified, our pilot shows that the system is flexible and scalable, and could potentially be adapted for many other low- and middle-income settings