Decision on mandating coverage for ART must begin with accurate data.

There are many stakeholders for school health data. Each one has a stake in the quality and accuracy of the health data collected and reported in schools. The joint NASN and NASSNC national school nurse data set initiative, Step Up & Be Counted!, heightens the need to assure accurate and precise data. The use of a standardized terminology allows the data on school health care delivered in local schools to be aggregated for use at the local, state, and national levels. The use of uniform terminology demands that data elements be defined and that accurate and reliable data are entered into the database. Barriers to accurate data are misunderstanding of accurate data needs, student caseloads that exceed the national recommendations, lack of electronic student health records, and electronic student health records that do not collect the indicators using the standardized terminology or definitions. The quality of the data that school nurses report and share has an impact at the personal, district, state, and national levels and influences the confidence and quality of the decisions made using that data.

The determination of suspended metals in coastal waters by different sampling and processing techniques (filtration, centrifugation)

This paper compares the accuracy of sources of measurement of four aircraft event times: pushback from the departure gate, take-off from the departing runway, landing at the arrival runway, and arrival at the arrival gate. These events are commonly referred to as (gate) Out, (wheels) Off, (wheels) On and (gate) In, or OOOI. OOOI times are significant because these times are used as the basis of predictions and in calculation of delays. For these and other reasons it is important that OOOI data have sufficient completeness and accuracy. The OOOI data analyzed herein cover Northwest Airlines (NWA) flights to and from Detroit Metropolitan Wayne County Airport (DTW) during May 1-30,2006. Surface surveillance radar track data were used as the reference truth for OOOI times. Aircraft Situation Data to Industry (ASDI) and OOOI data provided by NWA (Flight Event Data Store: FEDS) were compared to the OOOI event times extracted from the surface surveillance data. For a given flight the pertinent and available OOOI events were estimated or parsed from the different data sources and compared in a pairwise fashion. This kind of experimental design controls other sources of variability except that of interest, which in this instance are putative differences between data sources. The ASDI ON times contained in the AZ message header were found to be more accurate and much more frequent than the ON time contained in the A field of the AZ message. The FEDS data were expected to always be more accurate than ASDI data, but that was found to not always be true. The surface surveillance radar data provide independent and potentially the most accurate data on OOOI event times that may be more generally available compared to proprietary data (e.g., FEDS). Accurate OOOI data make possible the development of models for taxi-time of aircraft on the airport surface. The paper concludes with a brief exposition on the development of a departure taxi-time forecast model. Preliminary results suggest that a standard error of 2 minutes on the predicted departure taxi-time duration can be achieved for departures up to 10 minutes before the aircraft leave the ramp area

To identify the optimal sensor application method that gave the quickest display of accurate heart rate (HR) data using the Nellcor OxiMax N-600x pulse oximeter (PO). Stable infants who were monitored with an electrocardiograph were included. Three sensor application techniques were studied: (i) sensor connected to cable, then applied to infant; (ii) sensor connected to cable, applied to investigator's finger, and then to infant; (iii) sensor applied to infant, then connected to cable. The order of techniques tested was randomized for each infant. Time taken to apply the PO sensor, to display data and to display accurate data (HR(PO) = HR(ECG) ± 3 bpm) were recorded using a stopwatch. Forty infants were studied [mean (SD) birthweight, 1455 (872) g; gestational age, 31 (4) weeks; post-menstrual age, 34 (4) weeks]. Method 3 acquired any data significantly faster than methods 1 (p = 0.013; CI, -9.6 to -3.0 sec) and 2 (p = 0.004; CI, -5.9 to -1.2 sec). Method 3 acquired accurate data significantly faster than method 1 (p = 0.016; CI, -9.4 to -1.0 sec), but not method 2 (p = 0.28). Applying the sensor to the infant before connecting it to the cable yields the fastest acquisition of accurate HR data from the Nellcor PO.

Restoring a large-scale power system has always been a complicated and important issue. A lot of research work has been done on different aspects of the whole power system restoration procedure. However, more time will be required to complete the power system restoration process in an actual situation if accurate and real-time system data cannot be obtained. With the development of the wide area monitoring system (WAMS), power system operators are capable of accessing to more accurate data in the restoration stage after a major outage. The ultimate goal of the system restoration is to restore as much load as possible while in the shortest period of time after a blackout, and the restorable load can be estimated by employing WAMS. Moreover, discrete restorable loads are employed considering the limited number of circuit-breaker operations and the practical topology of distribution systems. In this work, a restorable load estimation method is proposed employing WAMS data after the network frame has been reenergized, and WAMS is also employed to monitor the system parameters in case the newly recovered system becomes unstable again. The proposed method has been validated with the New England 39-Bus system and an actual power system in Guangzhou, China.

Salinity is a major problem affecting the sustainability of agriculture and environment in the Rio Grande river basin in Texas, USA. An accurate data on the spatial distribution of salinity is necessary for developing effective salinity management practices. However, at present information is lacking on the amount and distribution of salts within the Rio Grande river basin. Manual methods of salinity assessment at high spatial resolution are labor intensive, time consuming and involve expensive laboratory analysis. Electromagnetic induction technique (EMI) can be a relatively low cost, non-invasive and rapid alternative for measuring and mapping soil salinity. This project evaluated utility, accuracy and factors affecting the accuracy of EMI technique to delineate salt affected areas within the Rio Grande river basin in the Far West Texas, USA. Results of this project indicated that the EMI technique provided accurate data on spatial distribution of salinity within a geographical location. Furthermore, accuracy of EMI data was influenced by factors such as amount of clay and soil moisture content at the time of EMI survey. Results also indicated that the EMI technique is rapid, cheaper, and provides accurate spatial data at a much higher resolution than the conventional manual method.

Biodiversity monitoring is key for developing informed solutions to the threats facing our environment, including habitat loss, increasing pollution, wildlife trafficking and climate change. Citizen science is increasingly used for collecting species observational data at wide spatial and temporal scales that are difficult and expensive to achieve using traditional means. Current apps used for citizen science biodiversity monitoring provide methods to record observational data on species’ presence, including photos, location, date, time and an assortment of other data. However, data about species absences as well as automatically generated and accurate data on both search effort and search locations have been lacking. Koala Counter is a free, cross-platform (Android & iOS), open-source app that was developed for a citizen science project to collect koala population data to inform koala conservation and management in South Australia. The app uses mobile phone sensors to transparently and automatically record metadata such as species observation location and time, the search path the user takes, the time taken while searching and GPS location precision. We tested this in the Citizen Science event “The Great Koala Count 2” in South Australia during November 2016. Observations, paths and search effort data were accurate overall. Location accuracy was good, with some exceptions. Use of the app indicated a number of potential improvements that would further increase data quality. Recording search paths offers a potentially valuable method of recording spatial and temporal components of search effort, improving on simple records of species observations and time taken, especially when no observations are made. These data may enable better ecological modelling by supplying accurate search effort data as well as enabling improved inference of species absence. Search paths also show locations that have not been searched, which is valuable information in management of citizen science monitoring programs.

Feasibility of AWHONN’s Immediate and Continuous Skin-to-Skin Nursing Care Quality Measures

2 University of Oklahoma College of Medicine/ Troy and Dolly Smith Cancer Center, Integris Baptist Medical Center, Oklahoma City, Oklahoma. A Brief History of Cancer Data in American Indians and Alaska Natives The risk for cancer has increased for American Indian and Alaska Native (AI/AN) populations since the beginning of the 20th century. This population is smaller in size and is younger than other racial/ ethnic groups, having survived the extermination policy of the US government in the 19th century and the decimation of the indigenous population by disease brought by the Europeans. Cancer statistics have not been easily obtained, nor accurate, for this special population. Tribal recognition, self-identification, and sporadic data collection concerning AI/AN populations have been fragmented. Only recently have improved data been prioritized by state health departments and hospital and central cancer registries.

THE HYGIENE STANDARD FOR CHRYSOTILE ASBESTOS

To analyse the costs and evaluate the equity, efficiency and feasibility of four strategies to identify poor households for premium exemptions in Ghana's National Health Insurance Scheme (NHIS): means testing (MT), proxy means testing (PMT), participatory wealth ranking (PWR) and geographic targeting (GT) in urban, rural and semi-urban settings in Ghana. We conducted the study in 145-147 households per setting with MT as our gold standard strategy. We estimated total costs that included costs of household surveys and cost of premiums paid to the poor, efficiency (cost per poor person identified), equity (number of true poor excluded) and the administrative feasibility of implementation. The cost of exempting one poor individual ranged from US$15.87 to US$95.44; exclusion of the poor ranged between 0% and 73%. MT was most efficient and equitable in rural and urban settings with low-poverty incidence; GT was efficient and equitable in the semi-urban setting with high-poverty incidence. PMT and PWR were less equitable and inefficient although feasible in some settings. We recommend MT as optimal strategy in low-poverty urban and rural settings and GT as optimal strategy in high-poverty semi-urban setting. The study is relevant to other social and developmental programmes that require identification and exemptions of the poor in low-income countries.

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To evaluate the reduction brought about by energy storage technology, it is essential to first have accurate data on carbon emissions from electricity consumption. However, when gathering this data by evaluating marginal emission factors (MEFs), previous research measured only generation emissions and direct transfer emissions while ignoring the impact of embodied emissions from the cross-grid transfer. To gather more accurate data, this study constructs an electricity network composed of 28 European countries in 2019 and compares the difference between the MEFs when considering the network-wide emissions and the MEFs when only considering generation emissions and direct transfer emissions for electricity trade (neglecting the indirect emissions in purchased electricity). Three energy storage strategies are adopted to evaluate the carbon emission reduction benefits of energy storage. The results show that the errors in emission accounting and MEF calculation are 7% and 10%, respectively, if the impact of electricity trade is not taken into account. When disregarding the indirect emissions from electricity trade, the errors in emission accounting and MEF calculation are 1%. Implementing wind curtailment reduction strategies for energy storage systems could effectively reduce electricity carbon emissions, more than 200 gCO2/kWh in most countries with 100% storage efficiency. The accuracy of MEFs has a significant impact on the results of energy storage benefits, and the choice of storage strategies has different effects on electricity emissions in the same country. Our methods have general applicability for other regions and countries.

Introduction Classical risk management includes three main phases:a hazard assessment including a hazard analysis (hazard characterization and frequency analysis) and a consequence analysis (consequence scenario and severity of consequences),a risk assessment (risk estimation and tolerance criteria), anda proper risk management plan through mitigation and feedback. These phases must be sequential but also iterative (Fig. 1). The hazard assessment gathers, organizes and summarizes all data relevant to risk assessment and management. It includes qualitative and quantitative characteristics of the hazard, addresses uncertainties and provides a range of forecasts based on plausible scenarios. Forecasts may be established as far as historical data reaches back, in terms of intensity and occurrence. Such forecasts are commonly derived for extreme metocean conditions, earthquakes and slope instabilities. The hazard is characterized in terms of probability of a measurable physical parameter, exceeding a certain threshold, during a period of time. A recurrence interval (or return period) is then defined. However, if there is no past experience with a hazard, there is no basis for any forecast. Such a conventional probabilistic approach cannot be applied to non-recurrent geohazards like Gas Hydrates (GH). GH present an inherent risk with no basis in their level of predictability (i.e. no return period). The GH hazard analysis (and the possible consequences) should then be evaluated based on the degree of knowledge and the related uncertainties. The approach is qualitative or semi-quantitative, as in the case of GH a quantitative approach is generally limited by the lack of extensive and accurate data and by the spatial variability of factors and parameters. This paper will review GH proxies used to identify critical parameters and scenarios, with particular attention paid to the limitation and the uncertainties in each of the proxies. A GH hazard-consequence risk assessment matrix is proposed, to be used as part of a global risk management process during the different phases of a deepwater E&P project (drilling and field development). Knowledge and uncertainties All hazard characterization relies upon the degree of understanding of the process, referred to as " the knowledge??. In the " Guide to Managing Project Risks??, the Project Management Institute (1992) has defined the well established " knowledge concept?? as follows. For the " known-knowns??, where all conditions are known and certain, decision making simplifies to an optimisation problem (if there is no uncertainty, there is no hazard). However, it may be impossible to remove all uncertainties, and there is a point where addition of more knowledge no longer reduces the uncertainty significantly. The " known-unknowns??, where we know a risk exists but we do not know how it may affect our facilities and where mitigation plans should be established. The " unknown-unknowns?? are catastrophic and unpredictable events. Uncertainty results from incomplete knowledge of a process and is related to our ability to understand, to measure and/or to describe the system. These uncertainties can be divided into three types:–Type 1: Uncertainties due to the variability of the earth, random by nature and inherent to the geological process that cannot be resolved even with additional data,–Type 2: Uncertainties characterized as ‘epistemic’, due to incomplete knowledge of geological process, which can be reduced through dedicated R&D projects, and in particular, model uncertainty that reflects the inability of a simulation model to represent precisely the true physical behaviour of the process,–Type 3: Uncertainties due to a lack of available / accurate data, and/or poor resolution. Such uncertainties include accuracy and precision of field data (measurement errors, limited, non-representative or unavailable data, data handling errors).

Annotated sequence data are instrumental in nearly all realms of biology. However, the advent of next-generation sequencing has rapidly facilitated an imbalance between accurate sequence data and accurate annotation data. To increase the annotation accuracy of the Caulobacter vibrioides CB13b1a (CB13) genome, we compared the PGAP and RAST annotations of the CB13 genome. A total of 64 unique genes were identified in the PGAP annotation that were either completely or partially absent in the RAST annotation, and a total of 16 genes were identified in the RAST annotation that were not included in the PGAP annotation. Moreover, PGAP identified 73 frameshifted genes and 22 genes with an internal stop. In contrast, RAST annotated the larger segment of these frameshifted genes without indicating a change in reading frame may have occurred. The RAST annotation did not include any genes with internal stop codons, since it chose start codons that were after the internal stop. To confirm the discrepancies between the two annotations and verify the accuracy of the CB13 genome sequence data, we re-sequenced and re-annotated the entire genome and obtained an identical sequence, except in a small number of homopolymer regions. A genome sequence comparison between the two versions allowed us to determine the correct number of bases in each homopolymer region, which eliminated frameshifts for 31 genes annotated as frameshifted genes and removed 24 pseudogenes from the PGAP annotation. Both annotation systems correctly identified genes that were missed by the other system. In addition, PGAP identified conserved gene fragments that represented the beginning of genes, but it employed no corrective method to adjust the reading frame of frameshifted genes or the start sites of genes harboring an internal stop codon. In doing so, the PGAP annotation identified a large number of pseudogenes, which may reflect evolutionary history but likely do not produce gene products. These results demonstrate that re-sequencing and annotation comparisons can be used to increase the accuracy of genomic data and the corresponding gene annotation.