Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.

Scientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event-definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and pre-classified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous-valued data set. ;Data and code were created using IDL, but can also be accessed with the open-source Gnu Data Language (GDL; see https://github.com/gnudatalanguage/gdl)

Green areas in urban areas are defined geographically by Statistics Sweden as part of the production of official statistics on green areas and green areas in urban areas. The data made available here is therefore primarily intended for the production of statistics. A green area is defined by Statistics Sweden as an area of continuous green space amounting to at least 0.5 hectares and which is generally accessible. Grassland counts as green areas, but not arable land. Green areas are geographically delimited to within urban areas. The minimum unit of account is 0.5 hectares. The definition thus does not take into account whether the areas are designated as green areas in the municipalities' master plans or detailed development plans. The delimitation of green areas is based on satellite data that is co-processed with geographic information and register data from Lantmäteriet. Data are available for two different reference dates, 2010 and 2015. The 2010 data covers only green areas in the 37 largest agglomerations according to the 2010 agglomeration delimitation. The 2015 data includes green areas in all agglomerations according to the 2015 agglomeration delimitation.

HD Lane Graph Data Services for OEMs Market Outlook

According to our latest research, the global market size for HD Lane Graph Data Services for OEMs reached USD 1.46 billion in 2024, with a robust growth trajectory driven by the increasing adoption of autonomous driving technologies and advanced vehicle safety systems. The market is forecasted to expand at a CAGR of 17.8% from 2025 to 2033, culminating in a projected value of USD 6.13 billion by 2033. This substantial growth is primarily fueled by the surging demand for high-definition mapping solutions, regulatory mandates for vehicle safety, and the rapid evolution of connected vehicles across global markets.

One of the primary growth factors propelling the HD Lane Graph Data Services for OEMs market is the accelerating pace of advancements in autonomous vehicle technologies. As automotive OEMs increasingly invest in Level 3 and above autonomous driving systems, the need for highly accurate and real-time lane-level mapping data has become critical. HD lane graph data enables vehicles to interpret complex road environments, recognize lane boundaries, and make safe navigation decisions even in challenging scenarios. The integration of AI and machine learning further enhances the capabilities of these mapping solutions, allowing for continuous updates and adaptive learning from real-world driving data. This technological synergy is stimulating OEMs to form strategic partnerships with mapping service providers and data analytics firms, further fueling market expansion.

Another significant driver for the market is the tightening regulatory landscape and growing emphasis on road safety. Governments and regulatory bodies across North America, Europe, and Asia Pacific are introducing stringent safety norms that mandate the incorporation of advanced driver assistance systems (ADAS) and autonomous functionalities in new vehicles. HD lane graph data services are foundational to the operation of features such as lane keeping assist, adaptive cruise control, and automated emergency braking. As these features become standard in both passenger and commercial vehicles, automotive OEMs are compelled to integrate high-definition mapping solutions to comply with regulations and enhance the overall safety profile of their vehicles. This regulatory push, in tandem with rising consumer expectations for safer and smarter mobility, is a major catalyst for market growth.

The proliferation of connected vehicles and the digital transformation of fleet management are also contributing significantly to market expansion. Fleet operators are increasingly leveraging HD lane graph data to optimize route planning, monitor driver behavior, and ensure compliance with safety protocols. The integration of real-time lane-level data into telematics platforms enables more efficient fleet operations, reduces accident rates, and minimizes operational costs. Additionally, the advent of Mobility-as-a-Service (MaaS) and shared mobility solutions is creating new avenues for the adoption of HD mapping technologies, as service providers seek to offer seamless and safe transportation experiences. This trend is expected to further amplify demand for HD lane graph data services in the coming years.

Regionally, North America continues to lead the HD Lane Graph Data Services for OEMs market, accounting for a significant share of global revenues in 2024. The region's dominance is underpinned by the presence of major automotive OEMs, technology innovators, and mapping service providers, coupled with proactive government initiatives supporting autonomous vehicle deployment. Europe follows closely, driven by strong regulatory frameworks and a robust automotive manufacturing base. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, increasing vehicle sales, and substantial investments in smart mobility infrastructure. These regional dynamics are shaping the competitive landscape and directing investments across the global market.

Service Type Analys

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the eighth publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in November 2011 and draw on 1.36 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (DH website). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

Abstract

[Extracted from Source http://www.customs.gov.lk]

Sri Lanka Customs is one of the oldest Government Departments, established in the year 1806. With the introduction of Customs Ordinance, it developed into a full-fledged state organization mainly responsible for the collection of revenue and the enforcement of Customs law. The functions of Customs Department include:

Collection of taxes, duties and other levies as imposed by the government Enforcement of tariff, trade and social protection policies of the state Ensuring flow of passenger, goods and related means of transport

Basically any type of general statistics what is published by Customs Department are released to the public. For example quantity, value and country of origin for any commodity imported or exported are released without any restriction. However Trade information of any importer or exporter are not released to a third party.

Processing of Customs Statistics is a continuous Administrative Record Keeping operation which the Data Processing Division of the Department of Census and Statistics had been handling on behalf of Sri Lanka Customs. The processed data are available in annual files (one magnetic file for each year) at the DP Division From 1974.

The customs statistics processed using the microdata are of enormous importance specially to Importers and Exporters of Sri Lanka. The data in respect of customs microdata are extracted from the Cusdec forms received by the Sri Lanka Customs as applications to transfer goods between Sri Lanka and other countries by the importers and exporters. The Cusdec form has gone through many changes with respect to the introduction/abolition of various taxes following the Government Budget directions. The microdata format, therefore has been altered to accommodate the changes whenever the need arose.

Geographic coverage

National coverage

The Department of Sri lanka Customs has offices geographically scattered in the island, such as the Ports, Air Ports, Free Trade Zones and other points along the sea belt.

Analysis unit

Each import/export item

Universe

All Importers, Exporters and Re-exporters who transfer goods between Sri Lanka and other countries.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

CusDec Information (Source www.customs.gov.lk as at 30th March 2009)

Box - A : Office use This area is only for office use, Customs clearance office at which the declaration is made and the documents are produced; Manifest reference, Customs reference number and Date will be given by officials as necessary

Cage No 01 : Declaration Type of declaration. All possible types of declaration (models of declaration) are shown in ACCESS guide IV; Chapter 3.

Cage No 02 : Exporter & TIN For Exports, Exporter in Sri Lanka, his/her name, address and VAT Number . For Importers, foreign suppliers name and address. As for foreigners not registered with Customs, VAT number is not applicable.

Cage No 03 : Pages Number of pages of the CusDec. the first potion is for its own page number and the next potion is for the total number of pages.

Cage No 04 : List Number of loading lists that come under one consignment. This cage is optional.

Cage No 05 : Items Total number of items of the Declaration.

Cage No 06 : Total Packages Total number of packages for the Declaration. Types of packages are not considered. Total number of packages may be consisted of different types of packages. The total must agree with the aggregate total number of packages for the items.

Cage No 07 : Declarant's Sequence Number System allocates a serial number for each CusDec submitted by a given declarant, which is unique for a year. Declarants are not required to fill this cage.

Cage No 08 : Consignee & VAT No For exports, name and address of the foreign consinee is entered but VAT number is not required. For imports, name, address & the VAT number of the consignee (importer in Sri Lanka) ahould be entered as shown in the documents.

Cage No 09 : Person responsible for financial statement & VAT Name, address and the VAT number (if applicable) of the person who is given authority by the consignee for financial setlement on behalf of the importer.

Cage No 10 : Country of Consignment/Country of first destination In case of imports, name and the code of the country from where the cargo had been shipped whereas for exports country of first destination.

Cage No 11 : Trading Country The name and the code of the country with which the financial transactions effected.

Cage No 12 : Value details If the FOB is used as the terms of payment, aggregate total of freight, insurance and other charges declared in local currency.

Cage No 13 : Reserved for future use

Cage No 14 : Declarant / Representative & VAT Name and address of the Declarant and his VAT number. The declarant is the person who lodge the declaration . He/She should be a "Registered Customs House Agent", acting with authority, on behalf of the importer / exporter.

Cage No 15 : Country of export / export code The name and the code of the country from where the cargo had been exported.

Cage No 16 : Country of origin The name of the country from where the cargo has originated (for example "Sri Lanka" can be entered for exports or local products). It is possible that a single shipment main contain commodities originating from more than one country, in which case the country from where the majority of commodities originated should be declared here.

Cage No 17 & 17A : Country of Destination/ Destination Code The name and the code of the country to which the cargo is sent. Ultimate destination ( this will be used for export or transit declaration only).

Cage No 18 : Vessel/Flight & Flag Name of the vessel or flight in which the cargo is imported or is to be exported. Flag is the country code that represents the nationality of the vessel/filight.

Cage No 19 : FCL (Container flag) This flag indicates whether the goods are containerized or not. For containerized goods the flag shold be set to 1 while for non containerized it should be set to 0.

Cage No 20 : Delivery terms Terms manually agreed upon by buyer and the seller in the international market in delivering or supplying the goods of import/export, are known as the term of delivery. The generally accepted terms of delivery fro Customs duty purposes are CIF (Cost, Insurance and Freight) for imports and FOB (Free on board) for exports, but the actual term of payment agreed upon by the buyer and seller may differ. (Please select the appropriate code from chapter 5 of ACCESS Guide IV).

Cage No 21 : Voyage No & Date Voyage number of the vessel/flight No. in which cargo is imported or to be exported and its date of arrival /departure.

Cage No 22 : Currency and total amount invoiced The first part of the cargo is for the code of currency in which the values are declared in the commercial invoice. The second part is for the total amount (CIF/FOB etc.) invoiced. If the value declared is FOB, the freight, insurance, and other charges should be declared in cage 6.

Cage No 23 : Exchange rate Current rate of exchange for the declared currency.

Cage No 24 : Nature of transaction Reserved for future use.

Cage No 25 : Mode of transport Code applicable to the mode of transport. In Sri Lanka, the mode of transport can only be Air, Sea or Posr (see chapter 6 of the ACCESS Guide IV).

Cage No 26 : Inland mode of transport Reserved for future use.

Cage No 27 : Place of loading/discharging Name of the port in Sri Lanka, at which the cargo is loaded/discharged.

Cage No 28 : Financial and banking data Bank Code Code of the bank through which the importer/exporter negotiates payment with the foreign supplier/buyer for the particular importation/exportation (see chapter 7 of the ACCESS Guide IV).

Terms of Payment terms mutually agreed upon by the buyer and the seller in the international market in makin the payment for supplying the goods for Import/Export. Only the terms of payments approved by the Controller of Exchange are permitted to be used for the means of transaction. i.e. Letter of Credit, DP terms, etc. (see chapter 4 of the ACCESS Guide IV).

Cage No 28A : Bank Name/Branch Name/Ref. No. Bank Name Name of the bank that represents the bank code in the cage number 28.

Branch Code Code of the bank branch given by the Central bank (see chapter 7 of the ACCESS Guide IV).

Reference Number Reference number

Cage No 29 : Office of Entry/Exit Code of Customs office at which the declaration (import/export) is made and documents are processed. These codes are known as Clearance Office Codes ( seechapter 2 of ACCESS Guide IV).

Cage No 30 : Location of goods The warehouse (Transit sheds) in which the cargo is kept until release from Customs charge ( This is not a mandatory input).

Cage No 31 : Package and description of goods Marks and Numbers Identification marks of the packages. The characters available in a type writer (key board) can only be used as marks and numbers.Initials or the abbreviated name of the consignee, country of destination, a reference number as agreed between the buyer and seller (if any) or the serial number of the package are the most common marks and numbers which are used in the International Trade.

Container No(s) If a particular consignment comes as a Full Container Load (FCL - Containerised cargo), its related container numbers should be declared in this cage. In the same time, cage number 19 should be set to 1 to indicate that the cargo is containerised.

Number and Kind Number of packages and the code of package type (see chapter 8 of the ACCESS Giude IV).

Description of goods Description of the commodity. Make sure to

BackgroundInformation and communication technology (ICT) has significantly advanced global healthcare, with electronic health (e-Health) applications improving health records and delivery. These innovations, including electronic health records, strengthen healthcare systems. The study investigates healthcare professionals’ perceptions of health information applications and their associated factors in the Cape Coast Metropolis of Ghana’s health facilities.MethodsWe used a descriptive cross-sectional study design to collect data from 632 healthcare professionals (HCPs), in the three purposively selected health facilities in the Cape Coast municipality of Ghana, in July 2022. Shapiro-Wilk test was used to check the normality of dependent variables. Descriptive statistics were used to report means with corresponding standard deviations for continuous variables. Proportions were also reported for categorical variables. Bivariate regression analysis was conducted to determine the factors influencing the Benefits of Information Technology (BoIT); Barriers to Information Technology Use (BITU); and Motives of Information Technology Use (MoITU) in healthcare delivery. Stata SE version 15 was used for the analysis. A p-value of less than 0.05 served as the basis for considering a statistically significant accepting hypothesis.ResultsHealthcare professionals (HCPs) generally perceived moderate benefits (Mean score (M) = 5.67) from information technology (IT) in healthcare. However, they slightly agreed that barriers like insufficient computers (M = 5.11), frequent system downtime (M = 5.09), low system performance (M = 5.04), and inadequate staff training (M = 4.88) hindered IT utilization. Respondents slightly agreed that training (M = 5.56), technical support (M = 5.46), and changes in work procedures (M = 5.10) motivated their IT use. Bivariate regression analysis revealed significant influences of education, working experience, healthcare profession, and IT training on attitudes towards IT utilization in healthcare delivery (BoIT, BITU, and MoITU). Additionally, the age of healthcare providers, education, and working experience significantly influenced BITU. Ultimately, age, education, working experience, healthcare profession, and IT training significantly influenced MoITU in healthcare delivery.ConclusionsHealthcare professionals acknowledge moderate benefits of IT in healthcare but encounter barriers like inadequate resources and training. Motives for IT use include staff training and support. Bivariate regression analysis shows education, working experience, profession, and IT training significantly influence attitudes towards IT adoption. Targeted interventions and policies can enhance IT utilization in the Cape Coast Metropolis, Ghana.

Zhang et al. (https://link.springer.com/article/10.1140/epjb/e2017-80122-8) suggest a temporal random network with changing dynamics that follow a Markov process, allowing for a continuous-time network history moving from a static definition of a random graph with a fixed number of nodes n and edge probability p to a temporal one. Defining lambda = probability per time granule of a new edge to appear and mu = probability per time granule of an existing edge to disappear, Zhang et al. show that the equilibrium probability of an edge is p=lambda/(lambda+mu) Our implementation, a Python package that we refer to as RandomDynamicGraph https://github.com/ScanLab-ossi/DynamicRandomGraphs, generates large-scale dynamic random graphs according to the defined density. The package focuses on massive data generation; it uses efficient math calculations, writes to file instead of in-memory when datasets are too large, and supports multi-processing. Please note the datetime is arbitrary.

Abstract

The NAC is a large-scale statistical investigation conducted periodically to collect, process and disseminate data on the structure of the agricultural sector.

Objectives: The general Objective of the VI National Agricultural Census Provide the country with basic, reliable information and significant on the agricultural reality, which allows strengthening its technical capacity to formulate plans, programs and development instruments, studies and policy analysis that tend to improve efficiency and effectiveness in the management of one of the sectors of greatest contribution to the national economy and the welfare of the population.

Specific Objectives 1. Collect, process, analyse, publish and disseminate census information on the agricultural sector. 2. Strengthen the technical and operational capacity of agricultural sector institutions to plan, organize and implement agricultural censuses and surveys. 3. Obtain the databases that allow for the updating of the sampling frameworks used for the collection of continuous agricultural surveys and for conducting intercensal statistical research. 4. Increase the levels of participation through adequate encouragement, both of technical and administrative staff of the participating institutions and of users, to achieve better training and use of the sector's information.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the NAC 2014 was the agricultural farm, defined as "any land area, dedicated totally or partially to agricultural production for sale or self-consumption, managed by a household, society, company, public institution or other, whose tasks may be coordinated or directly accomplished by a person or with the help of others, and whose activities are carried out under the same management, by using the same means of production, such as labour, machinery, equipment and work animals". The definition of agricultural holdings includes aquaculture and forestry activities. Therefore, if the holding has only one those activities, it is considered as an agriculture holding. The farm can be constituted by one or more plots, under property or under other type of tenure, and may be located together or separately from one another, in the same canton or in different cantons. It includes family gardens, hydroponics and organoponics agricultural systems. The agricultural producer is the individual or legal entity that assumes full economic responsibility in the management of the farm and that may or may not have technical functions; it may manage the farm personally or exercise this function through another person who administers it.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The NAC 2014 used the classical approach.

ii. Frame Data from the Ministry of Agriculture and Livestock were a source for the agricultural census frame. The cartography of the National Population and Housing Census 2011 was used for the NAC 2014.

iii. Complete and/or sample enumeration methods The complete enumeration method was used for the NAC 2014.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single questionnaire was applied to collect all census information. Compared to the previous NAC, the NAC 2014 collected new items, such as data on irrigation and drainage practices, agricultural practices, agricultural equipment and infrastructure, agricultural support services, and variables related to food security and the environment. The sixth NAC covered 15 of the 16 core items recommended in the WCA 2010. The item "Other economic production activities of the holding's enterprise" was not covered.

Cleaning operations

1. DATA PROCESSING AND ARCHIVING Data entry was done by scanning the questionnaires. The data processing programme was developed by INEC using C# and CSPro. A designing, processing and analysis team supported this process. Microsoft SQL Server was used as a database management system. Census data were exported using SPSS.

2. CENSUS DATA QUALITY To ensure quality in the design and preparation of the census, several field tests in different parts of the country were carried out, including the 2013 pilot agricultural census, in which the following were evaluated: the functionality of the census questionnaire, the productivity of the enumerator during the interview, the communication strategy, etc.

The Travel Monitoring Analysis System (TMAS) - Class dataset was updated on December 31, 2023 and was created on July 16, 2024 by the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TMAS Continuous Vehicle Classification (CVC) data represents both the FHWA 13 vehicle types and length-based class as defined by the 2001 Traffic Monitoring Guide (TMG). For more information consult https://doi.org/10.21949/1519109 A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529345

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the second publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in May 2011 and draw on just over 1.4 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (external link). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the fourth publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in July 2011 and draw on 1.4 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following 5 A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions. The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in March 2012 and draw on 1.53 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following five A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (external link). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.