The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Adherence to treatment is critical to effective management of COPD and is key to addressing the growing burden of disease. So, this study condcuted to evaluate the effectiveness of a health education intervention on treatment adherence behavior of COPD outpatients and identify the level of health status improvement.A random control trial was conducted in 2022 at two respiratory outpatient clinics in Da Nang City, Vietnam with the identification number is TCTR20240526001. 90 participants were divided into two group, 45 members of the health education group who received 5-times consulting about disease knowledge, training using inhallers, breathing exercises at clinic, then 12-times tele-consultation; and 45 controls who only joined surveys. Treatment adherence and level of health status improvement were assessed as outcomes.Inclusion criteria: Patients were diagnosed with COPD according to GOLD 2018 criteria; Received stable home treatment with inhaled medications; No acute episodes, including acute episodes due to chronic diseases requiring hospitalization for at least 3 months; Able to speak, read and understand Vietnamese; Participants had and knew how to use a smartphone with an Internet connection; Voluntarily participated in the study.Exclusion criteria: History of bronchial asthma, allergic rhinitis, lung surgery, or respiratory diseases; People with mental disorders or other serious illnesses.Research time: The pre-intervention data collection period spans from April 2021 to October 2021; Content development for the intervention occurs from November 2021 to April 2022; The intervention itself takes place from April 2022 to June 2022, lasting three months; and Post-intervention assessments are conducted from July 2022 to August 2022.InterventionThe intervention conducted from 01/03/2022 to 25/12/2022.Participants in the intervention group participated in two topical discussions with the research team at the hospital's outpatient clinic. Within 60 to 90 minutes, patients were provided with knowledge about the disease, practical skills in using inhaled drugs, instructions on breathing exercises, and self-management skills to improve their treatment adherence. The second session was conducted one week later (see Table 1). In addition, after completion, each patient would be given and instructed to maintain medicine use and breathing exercise diary. The diary was collected after the intervention ended and considered the basis for assessing participants' treatment adherence level. Face-to-face meetings were still conducted once a month for three continuously 3 months when the patient came for a regular appointment. This outline program has assessed by healthcare managers and practicing nurses with high scores of acceptability, appropriateness, and feasibility (M = 4.31; SD = 0.11) and (M = 4.37; SD = 0.12), respectively.The online home monitoring process was conducted immediately afterward and continued for 3 months. Periodically once a week during the hours of 8 - 9 am on Wednesday of the week, the research team made group phone calls (5 patients/group) via Zalo software; the time for each phone call was around 3-5 minutes and no more than 10 minutes. For patients who did not participate in the group call, the researcher called via their personal phone number to remind them to participate. The private call would be made three times, each time five minutes apart, to ensure the group call had enough participants. Participants were instructed not to tell and/or share phone calls contents with others. During these calls, each participant self-reported medication history, side effects of the drug (if any), the process of performing breathing exercises at home, common symptoms when performing therapy, number of dyspnea/weeks, amount and color of sputum. In addition, the research team also provided some health information such as measures to deal with dyspnea, reminding patients to practice these exercises, giving advice to quit smoking, practice inhalation with Sopiroball, practice coughing effectively, and reminding patients to record information in the diary, send a video of their breathing exercises for us to monitor and support. In all group phone calls, we maintained a consistent structure by implementing the exchanges mentioned above to ensure uniformity across all calls.Data collectionThe primary outcome in this study was treatment adherence (including adherence to inhaled medications and adherence to breathing exercises), and the secondary outcome was the proportion of participants with health status enhancement (assessing by disease severity, degree of dyspnea, and degree of airway obstruction). Primary and secondary outcomes were measured at study baseline and three months later. Treatment adherence was assessed in two ways, including adherent to inhaled drugs, and adherent to breathing exercises. Assessment of adherence to inhaled drugs by the Test of Adherence to Inhalers (TAI-10) that developed by Plaza et al., consisted of 10 questions [20]. Each item is based on a 5-Likert scale that ranges from 1- worst to 5 - best adherence. The total score was from 10 to 50 points, in which patients were seen as adhering with a score ranging from 46 to 50, and non-adherence for a score ≤ 45. The questionnaire was testing reliability with high score (Cronbach alpha at 0.871), and the test-retest reliability coefficient for the total sum score was 0.832 (p < 0.01). Additionally, adherence to breathing exercises was assessed based on successful practice as well as maintaining the frequency of daily breathing exercises. Patients were seen as adherence if they did the correct all steps through the practice checklist of breathing exercises and did one or more times per day within 10 to 15 minutes per time, and/or gradually increased by their own ability. Non-adherence was recorded for the patient if did not maintain daily practice or maintains daily practice but practiced with "fail" result. Finally, the patient was assessed as adherence to treatment if there was concurrent adherence with inhaled drugs and breathing exercise therapy; conversely, non-adherent if adherence one of two contents or non-adherence with both. The patient’s health status was assessed through two indicators, including severity of disease and degree of airway obstruction. For the first indicator, it was assessed through the modified Medical Research Council (mMRC) scale and the COPD Assessment Test (CAT). Patients were considered "Mild disease" if mMRC 0-1 and CAT10. The mMRC scale was tested with had good validity and reliability, while CAT scale was assessed with Cronbach’s alpha coefficient of 0.924. The second indicator was a degree of airway obstruction that was measured by a spirometer, and the result of each patient was classified as:“Mild - Moderate” with FEV1≥ 80% and 50%.

Background: At times, electronic medical records (EMRs) have proven to be less than optimal, causing longer hours behind computers, shorter time with patients, suboptimal patient safety, provider dissatisfaction, and physician burnout. These concerning healthcare issues can be positively affected by optimizing EMR usability, which in turn would lead to substantial benefits to healthcare professionals such as increased healthcare professional productivity, efficiency, quality, and accuracy. Documentation issues, such as non-standardization of physician note templates and tedious, time-consuming notes in our mother-baby unit (MBU), were discussed during meetings with stakeholders in the MBU and our hospital's EMR analysts.Objective: The objective of this study was to assess physician note optimization on saving time for patient care and improving provider satisfaction.Methods: This quality improvement pilot investigation was conducted in our MBU where four note templates were optimized: History and Physical (H and P), Progress Note (PN), Discharge Summary (DCS), and Hand-Off List (HOL). Free text elements documented elsewhere in the EMR (e.g., delivery information, maternal data, lab result, etc.) were identified and replaced with dynamic links that automatically populate the note with these data. Discrete data pick lists replaced necessary elements that were previously free texts. The new note templates were given new names for ease of accessibility. Ten randomly chosen pediatric residents completed both the old and new note templates for the same control newborn encounter during a period of one year. Time spent and number of actions taken (clicks, keystrokes, transitions, and mouse-keyboard switches) to complete these notes were recorded. Surveys were sent to MBU providers regarding overall satisfaction with the new note templates.Results: The ten residents' average time saved was 23 min per infant. Reflecting this saved time on the number of infants admitted to our MBU between January 2016 and September, 2019 which was 9373 infants; resulted in 2.6 hours saved per day, knowing that every infant averages two days length of stay. The new note templates required 69 fewer actions taken than the old ones (H and P: 11, PN: 8, DCS: 18, HOL: 32). The provider surveys were consistent with improved provider satisfaction.Conclusion: Optimizing physician notes saved time for patient care and improved physician satisfaction.

Dataset description This dataset is part of a bigger dataset of intracranial EEG (iEEG) called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands. It consists of 12 patients: six patients recorded intraoperatively using electrocorticography (acute ECoG), six patients with long-term recordings (3 patients recorded with ECoG and 3 patients recorded with stereo-encephalography SEEG). For a detailed description see (link to bids paper).

This data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/

Each patient has their own folder (e.g., sub-RESP0280) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.

Two different implementation of the BIDS structure were done according to the different type of recordings (i.e. intraoperative or long-term) Intraoperative ECoG Surgery with intraoperative ECoG is composed of three main situations that can be logically grouped into BIDS sessions:

• Pre-resection sessions, consisting of all recordings (with different configurations of the grid and strips/depth) carried out before the surgeon has started the planned resection.

• Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area.

• Post-resection sessions, consisting of all the recordings performed after the last resection.

Each situation is labelled with an increasing number starting from 1, indicative of the period in time respective to the surgical resection and a consecutive letter (starting from A) indicative of the position of the grid and strip/depth for a given session. As an example see patient RESP0280 who had 4 sessions recorded: two pre-resection sessions, one intermediate sessions and one post-resection session. The first session is SITUATION1A consisting of the first recording, then the grid was moved to another position, resulting in SITUATION1B. After that, the surgeon resected part of the brain and then there was another recording(SITUATION2A). Finally the surgeon applied a resection for the last time and the recording after that was defined as SITUATION3A.

Long-term iEEG In long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period. If extra electrodes were added/removed during this period, the session was divided into different sessions (e.g. ses-1A and ses-1b). We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15). The task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. Different tasks have been defined, such as “rest” when a patient is awake but not doing a specific task, “sleep” when a patient is sleeping the majority of the file, or “SPESclin” when the clinical SPES protocol has been performed in this file. Other task definitions can be found in the annotation syntax (https://github.com/UMCU-EpiLAB/umcuEpi_longterm_ieeg_respect_bids/master/manuals/IFU_annotatingtrc_ECoG).

License This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/); in particular, while not legally required, we hope that all users of the data will acknowledge by citing Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS”, submitted to NeuroInformatics in 2020, in any publications.

Code available at: https://github.com/UMCU-EpiLAB.

Acknowledgements We would like to thank the patients for providing their data for this dataset, the RESPect team of University Medical Center of Utrecht, for the acquisition of the dataset. Please cite Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS”, submitted to NeuroInformatics in 2020, in any publications.

BackgroundThe European Society of Cardiology guidelines recommend (Class IA) single-time–point screening for atrial fibrillation (AF) using pulse palpation. The role of pulse palpation for AF detection has not been validated against electrocardiogram (ECG) recordings. We aimed to study the validity of AF screening using self-pulse palpation compared with an ECG recording conducted at the same time using a handheld ECG 3 times a day for 2 weeks.Methods and findingsIn this cross-sectional screening study, patients 65 years of age and older attending 4 primary care centers (PCCs) outside Stockholm County were invited to take part in AF screening from July 2017 to December 2018. Patients were included irrespective of their reason for visiting the PCC. Handheld intermittent ECGs 3 times per day were offered to patients without AF for a period of 2 weeks, and patients were instructed in how to take their own pulse at the same time. A total of 1,010 patients (mean age 73 years, 61% female, with an average CHA2DS2-VASc score 2.9) participated in the study, and 27 (2.7%, 95% CI 1.8%–3.9%) new cases of AF were detected. Anticoagulants (ACs) could be initiated in 26 (96%, 95% CI 81%–100%) of these cases. A total of 53,782 simultaneous ECG recordings and pulse measurements were registered. AF was verified in 311 ECG recordings, of which the pulse was palpated as irregular in 77 recordings (25%, 95% CI 20%–30% sensitivity per measurement occasion). Of the 27 AF cases, 15 cases felt an irregular pulse on at least one occasion (56%, 95% CI 35%–75% sensitivity per individual). 187 individuals without AF felt an irregular pulse on at least one occasion. The specificity per measurement occasion and per individual was (98%, 95% CI 98%–98%) and (81%, 95% CI 78%–83%), respectively.ConclusionsAF screening using self-pulse palpation 3 times daily for 2 weeks has lower sensitivity compared with simultaneous intermittent ECG. Thus, it may be better to screen for AF using intermittent ECG without stepwise screening using pulse palpation. A limitation of this model could be the reduced availability of handheld ECG recorders in primary care centers.

The 4 km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5 sea surface temperature (SST) dataset is a reanalysis of historical AVHRR data that have been improved using extensive calibration, validation and other information to yield a consistent research quality time series for global climate studies. This SST time series represents the longest continual global ocean physical measurement from space. Development of the Pathfinder dataset is sponsored by the NOAA National Oceanographic Data Center (NODC) in collaboration with the University of Miami Rosensteil School of Marine and Atmospheric Science (RSMAS) while distribution is a collaborative effort between the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the NODC. From a historical perspective, the Pathfinder program was originally initiated in the 1990s as a joint NOAA/NASA research activity for reprocessing of satellite based data sets including SST. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having an operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. For the Pathfinder SST algorithm only the 11 and 12 micron channels are used. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. The collection of NOAA satellite platforms used in the AVHRR Pathfinder SST time series includes NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-17, and NOAA-18. These platforms contain "afternoon" orbits having a daytime ascending node of between 13:30 and 14:30 local time (at time of launch) with the exception of NOAA-17 that has a daytime descending node of approximately 10:00 local time. SST AVHRR Pathfinder includes separate daytime and nighttime daily, 5 day, 8 day, monthly and yearly datasets. This particular dataset represent daytime monthly averaged observations.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is the last calendar day in the reporting period. Until July 2021, the snapshots were produced each quarter and were a record as of 31 March, 30 June, 30 September, and 31 December. However, we now collect and publish data on the general practice workforce on a monthly basis and the snapshot therefore relates to the last calendar day of each month, including weekends and public holidays. This monthly snapshot reflects the general practice workforce at 30 April 2022. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes CCG-level FTE and headcount breakdowns for the current reporting period. CSVs containing practice-level summaries and CCG-level counts of individuals are also available. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. In addition to the snapshot of the main general practice workforce, Annexes B and C in the Excel Bulletin tables include figures relating to the number of ad-hoc locum GPs working in general practice and information about their working hours. These figures used to be included in the main totals, but data relating to the ad-hoc locum workforce is collected differently and these figures do not constitute a snapshot. As a result, because they are not directly comparable to the snapshot, we now report these figures separately rather than including them in the overall totals. In May 2022 we introduced a quarterly publication using additional data sources to complement this publication, which brings together FTE staff working in general practice, including ad-hoc locums, and those working in Primary Care Networks. The first experimental edition was released on 19 May, initially presenting FTE primary care workforce statistics for the direct patient care, nurse and admin/non-clinical staff groups. The GP staff group is scheduled for release on 16 June, once final ad-hoc locum data becomes available. See https://digital.nhs.uk/data-and-information/publications/statistical/primary-care-workforce-quarterly-update for more information. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

To describe clinical characteristics and regional treatment patterns of episodic cluster headache (CH). A point-in-time survey of physicians and their patients with CH was conducted in the United States, United Kingdom and Germany in 2017. Overall, 1012 patients with episodic CH were analyzed. Demographic and clinical findings were generally consistent across regions. Most patients were men (66.6%) and the mean age was 40.9 years. The greatest proportion of patients (38.3%) had ≤1 attack per day. The mean number of attacks per day (APD) was 2.4 and mean number of cluster periods per year was 2.6; the mean cluster period duration was 30.8 days. Most patients (69.3%) did not report a specific or predicable time when cluster periods occurred. Acute treatment was prescribed for 47.6% of patients, 10.3% of patients received preventive treatment, and 37.9% of patients received combined acute and preventive treatment; 4.2% of patients were not receiving treatment. Frequently prescribed acute treatments were sumatriptan, oxygen, and zolmitriptan; oxygen use varied considerably across countries and was prescribed least often in the United States. Frequently prescribed preventive treatments were verapamil, topiramate, and lithium. Lack of efficacy and tolerability were the most common reasons for discontinuing preventive treatment. We observed high use of acute treatments, but only half of patients used preventive treatments despite experiencing several cluster periods per year with multiple cluster APD. Further studies about the need for and benefits of preventive treatment for episodic CH are warranted. People with cluster headache (CH) experience headache attacks of excruciating stabbing pain, usually on one side of the head around the eye. These headache attacks typically last between 15 min and 3 h, and come in clusters (or bouts) occurring up to several times a day for a few weeks or months at a time. This greatly impacts a patient’s quality of life. We surveyed doctors and their patients across the United States, the United Kingdom and Germany, looking at symptoms that occurred during CH attacks, how long the headache attacks lasted, how often the patient had them, and what medicines were being given. Our results showed that patients with CH suffered from clusters (bouts) of headache attacks several times a year. Nearly, a third of patients had a wrong diagnosis before being diagnosed with CH. Patients experienced stress, agitation, restlessness, difficulty relaxing and depression during a headache attack, especially those who had more CH attacks each day. Although many patients were taking medication, only half of patients were prescribed medicines to prevent their headache attack from starting. Side effects and the medicines not working were the most common reasons patients stopped taking medicine to prevent their headache attacks. The differences seen in medicines prescribed between countries suggest differences in guidance, or in doctors’ awareness of current medication guidelines. Further studies about the need for and benefits of medicines to prevent CH attacks are needed.

China Residents’ Average Daily Time Use: Primary Activity Domain: Essential Personal Physiological Activities data was reported at 747.000 min in 2024. This records an increase from the previous number of 713.000 min for 2018. China Residents’ Average Daily Time Use: Primary Activity Domain: Essential Personal Physiological Activities data is updated yearly, averaging 730.000 min from Dec 2018 (Median) to 2024, with 2 observations. The data reached an all-time high of 747.000 min in 2024 and a record low of 713.000 min in 2018. China Residents’ Average Daily Time Use: Primary Activity Domain: Essential Personal Physiological Activities data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Residents’ Average Daily Time Use.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices: As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides a map depicting the latest daily sea surface temperature analyses from both the NOAA/NWS/NCEP operational Real-Time Global SST Analysis System, commonly referred to as RTG_SST, and the NASA/SPoRT experimental Sea Surface Temperature Composite.

The RTG_SST has a 1/12 degree (~9 km) grid resolution and covers the globe including the Great Lakes. SSTs are indicated by different colors at 2 degrees F intervals. NCEP generates the analysis once per day and it is updated on the nowCOAST™ map service around 0400 UTC (11 PM EST).

The experimental SPoRT SST analysis has a 2 km grid resolution and covers the North Atlantic Ocean and the Eastern North Pacific Ocean, the Great Lakes, and occasionally other large lakes. SSTs are displayed by the same color scale used for the RTG_SST analysis. NASA generates the analysis twice per day and it is updated on the nowCOAST™ map service around 0330 UTC (2230 EST) and 1530 UTC (1030 EST). For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.

Background Information

The NWS/NCEP daily SST (1/12 deg) analysis is generated by the NCEP RTG_SST Analysis System using a two-dimensional variational interpolation scheme. The interpolation scheme uses the most recent 24-hours buoy and ship data and U.S. Navy SEATEMP (SST) retrievals derived from AVHRR data from the NOAA polar orbiting satellites. The first guess for the interpolation scheme is provided by the un-smoothed analysis from the previous day with a one-day climate adjustment. The analysis system was developed by the NWS/NCEP/Environmental Modeling Center/Marine Modeling and Analysis Branch.

The NASA/SPoRT experimental SST Composite is a blend of the MODIS and NESDIS SST products except over the Great Lakes, where it is a blend of the MODIS and the United Kingdom Met Office (UKMO) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIAS2). The NESDIS and OSTIAS2 products have spatial resolutions of 9 and 6 km, respectively. The compositing algorithm uses a seven-day collection of MODIS level-2B data and the most recent NESDIS GOES/POES SST Composite and OSTIAS2 daily products. Two types of weighting are used in the compositing process. One weight is for the data latency and the other for the product type. The MODIS data with a 1-km resolution is given the most weight. All available confidence flags and bias information are incorporated in the compositing process. The SST Composite is computed twice-daily (nighttime and daytime). The MODIS and OSTIA products are obtained in near-real-time from the GHRSST archive at NASA/JPL. The compositing system was developed by NASA Short-Term Prediction Research and Transition Center (SPoRT) Team.

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

  Jedlovec, G.J., F. LaFontaine, J. Shafer, J. Vazquez, E. Armstrong, and M. Chin, 2009:
  An Enhanced MODIS / AMSR-E SST Composite Product, GHRSST User Symposium, Santa Rosa, CA.


  NASA, 2014: Sea Surface Temperature (SST) Product Details (Available at http://weather.msfc.nasa.gov/sport/sst/descriptions.html)


  NWS, 2001: The Real-Time Global Sea Surface Temperature Analysis: RTG_SST, NWS Technical Procedures Bulletin Series No. 477, NWS, Silver Spring, MD
  (Available at http://www.nws.noaa.gov/om/tpb/477.pdf)

The B.C. COVID-19 Dashboard has been retired and will no longer be updated.Purpose: These data can be used for visual or reference purposes.British Columbia, Canada COVID-19 Regional Summary Date are from the British Columbia Centre for Disease Control, Provincial Health Services Authority and the British Columbia Ministry of Health.

These data represent the British Columbia Health Service Delivery Area and Health Authority 7-day Moving Average COVID-19 case data.

These data were made specifically for the British Columbia COVID-19 Dashboard.

Terms of use, disclaimer and limitation of liabilityAlthough every effort has been made to provide accurate information, the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health makes no representation or warranties regarding the accuracy of the information in the dashboard and the associated data, nor will it accept responsibility for errors or omissions. Data may not reflect the current situation, and therefore should only be used for reference purposes. Access to and/or content of these data and associated data may be suspended, discontinued, or altered, in part or in whole, at any time, for any reason, with or without prior notice, at the discretion of the Province of British Columbia.Anyone using this information does so at his or her own risk, and by using such information agrees to indemnify the Province of British Columbia, including the British Columbia Centre for Disease Control, the Provincial Health Services Authority and the British Columbia Ministry of Health and its content providers from any and all liability, loss, injury, damages, costs and expenses (including legal fees and expenses) arising from such person’s use of the information on this website.Dashboard Updates - GeneralData are updated up to the previous Saturday. Weekly metrics reflect the latest full week, Sunday to Saturday. The “Currently Hospitalized” and “Currently in Critical Care” reflect daily volumes on the Thursday.Data Notes - GeneralThe following data notes define the indicators presented on the public dashboard and describe the data sources involved. Data changes as new cases are identified, characteristics of reported cases change or are updated, and data corrections are made. Specific values may therefore fluctuate in response to underlying system changes. As such, case, hospitalization, deaths, testing and vaccination counts and rates may not be directly comparable to previously published reports. For the latest caveats about the data, please refer to the most recent BCCDC Surveillance Report located at: www.bccdc.ca/health-info/diseases-conditions/covid-19/dataData SourcesLaboratory data are supplied by the B.C. Centre for Disease Control (BCCDC) Public Health Laboratory; tests performed for other provinces have been excluded. See “Data Over Time” for more information on changes to the case definition.Total COVID-19 cases include lab-confirmed, lab-probable and epi-linked cases. Case definitions can be found at: https://www.bccdc.ca/health-professionals/clinical-resources/case-definitions/covid-19-(novel-coronavirus). Currently hospitalized and critical care hospitalizations data are received from Provincial COVID-19 Monitoring Solution, Provincial Health Services Authority. See “Data Over Time” for more information on previous data sources.Vaccine data are received from the B.C. Ministry of Health.Mortality data are received from Vital Statistics, B.C. Ministry of Health. See Data Over Time for more information on precious data sources.Laboratory data is supplied by the B.C. Centre for Disease Control Public Health Laboratory and the Provincial Lab Information Solution (PLIS); tests performed for other provinces have been excluded.Critical care hospitalizations are provided by the health authorities to PHSA on a daily basis. BCCDC/PHSA/B.C. Ministry of Health data sources are available at the links below:Cases Totals (spatial)Case DetailsLaboratory Testing InformationRegional Summary DataData Over TimeThe number of laboratory tests performed and positivity rate over time are reported by the date of test result. See “Laboratory Indicators” section for more details.Laboratory confirmed cases are reported based on the client's first positive lab result.As of April 2, 2022, cases include laboratory-diagnosed cases (confirmed and probable) funded under Medical Services Plan.From January 7, 2021 to April 1, 2022, cases included those reported by the health authorities and those with positive laboratory results reported to the BCCDC. The number of cases over time is reported by the result date of the client's first positive lab result where available; otherwise by the date they are reported to public health. Prior to April 2, 2022, total COVID-19 cases included laboratory-diagnosed cases (confirmed and probable) as well as epi-linked cases. Prior to June 4, 2020, the total number of cases included only laboratory-diagnosed cases.As of January 14, 2022, the data source for "Currently Hospitalized" has changed to better reflect hospital capacity. Comparisons to numbers before this date should not be made.As of April 2, 2022, death is defined as an individual who has died from any cause, within 30 days of a first COVID-19 positive lab result date. Prior to April 22, 2022, death information was collected by Regional Health Authorities and defined as any death related to COVID-19. Comparisons between these time periods are not advised.Epidemiologic Indicators"Currently Hospitalized" is the number of people who test positive for COVID-19 through hospital screening practices, regardless of the reason for admission, as recorded in PCMS on the day the dashboard is refreshed. It is reported by the hospital in which the patient is hospitalized, rather than the patient's health authority of residence.Critical care values (intensive care units, high acuity units, and other critical care surge beds) include individuals who test positive for COVID-19 and are in critical care, as recorded in PCMS.The 7-day moving average is an average daily value over the 7 days up to and including the selected date. The 7-day window moved - or changes - with each new day of data. It is used to smooth new daily case and death counts or rates to mitigate the impact of short-term fluctuations and to more clearly identify the most recent trend over time.The following epidemiological indicators are included in the provincial case data file:Date: date of the client's first positive lab result.HA: health authority assigned to the caseSex: the sex of the clientAge_Group: the age group of the clientClassification_Reported: whether the case has been lab-diagnosed or is epidemiologically linked to another caseThe following epidemiological indicators are included in the regional summary data file:Cases_Reported: the number of cases for the health authority (HA) and health service delivery area (HSDA)Cases_Reported_Smoothed: Seven day moving average for reported casesLaboratory IndicatorsTests represent the number of all COVID-19 tests reported to the BCCDC Public Helath Laboratory since testing began mid-January 2020. Only tests for residents of B.C. are included.COVID-19 positivity rate is calculated for each day as the ratio of 7-day rolling average of number of positive specimens to 7-day rolling average of the total number of specimens tested (positive, negative, indeterminate and invalid). A 7-day rolling average applied to all testing data corrects for uneven data release patterns while accurately representing the provincial positivity trends. It avoids misleading daily peaks and valleys due to varying capacities and reporting cadences.Turn-around time is calculated as the daily average time (in hours) between specimen collection and report of a test result. Turn-around time includes the time to ship specimens to the lab; patients who live farther away are expected to have slightly longer average turn around times.The rate of COVID-19 testing per million population is defined as the cumulative number of people tested for COVID-19/B.C. population x 1,000,000. B.C. Please note: the same person may be tested multiple times, thus it is not possible to derive this rate directly from the number of cumulative tests reported on the B.C. COVID-19 Dashboard.Testing context: COVID-19 diagnostic testing and laboratory test guidelines have changed in British Columbia over time. B.C.'s testing strategy has been characterized by four phases: 1) Exposure-based testing (start of pandemic), 2) Targeted testing (March 16, 2020), 3) Expanded testing (April 9, 2020), 4) Symptom-based testing (April 21, 2020), and 5) Symptom-based testing for targeted populations (a-are at risk of more severe disease and/or b-live or work in high-risk settings such as healthcare workers) and Rapid Antigen Tests deployment (January 18, 2022). Due to changes in testing strategies in BC in 2022, focusing on targeted higher risk populations, current case counts are an underestimate of the true number of COVID-19 cases in BC and may not be representative of the situation in the community.
The following laboratory indicators are included in the provincial laboratory data file:New_Tests: the number of new COVID-19 testsTotal_Tests: the total number of COVID-19 testsPositivity: the positivity rate for COVID-19 testsTurn_Around: the turnaround time for COVID-19 testsBC Testing Rate: Total PCR + POC tests per day (excluding POC that were confirmed by PCR within 7 days) / Population using BC Stats PEOPLE2021 population projections for the year 2022 * 100,000.Health Authority AssignmentCases are reported by health authority of residence.As of April 2, 2022, cases are reported based on the address provided at the time of testing; when not available, by location of the provider ordering the lab test.As of April 2, 2022,

What is this summary about? This is a summary of an article originally published in the American Journal of Health-System Pharmacy. Hospital-at-Home (HaH) involves giving hospital-type care to patients at home. At home, patients often need injections. Injections can be given under the skin or into the muscle or vein with a needle. An injection could be given quickly or over longer periods of time. Patients, caregivers, doctors, nurses, pharmacists, and other healthcare staff face many problems in managing such injections in HaH. What are the key takeaways? A doctor or nurse normally gives an injection to a patient. Each time an injection needs to be given, doctors or nurses may need to visit patients’ homes. If an injection needs to be given many times a day, this means many visits to patients’homes. To manage this, sometimes patients and caregivers are trained to give injections by themselves. Some injections may be given over a longer time or as continuous infusions through a pump. This allows fewer visits by healthcare staff. Transport and storage of such injections at home also needs special care. In the same way, some medicines that need to be stored safely require special care. Care should be taken to avoid risk of infection from regular visits of doctors and healthcare staff.Medical waste must be carefully disposed of to prevent pollution. What were the main conclusions reported by the researchers? This study describes the authors’ view of the problems with using injections, along with suggested solutions. These solutions may help healthcare staff and benefit patients and caregivers involved in HaH care. Who is this article for? This study may be helpful for patients receiving HaH care and their caregivers,and healthcare staff involved in HaH. This is an abstract of the Plain Language Summary of Publication article. View the full Plain Language Summary PDF of this article to read the full-text

BackgroundWith the current treatment options for visceral leishmaniasis (VL), recrudescence of the parasite is seen in a proportion of patients. Understanding parasite dynamics is crucial to improving treatment efficacy and predicting patient relapse in cases of VL. This study aimed to characterize the kinetics of circulating Leishmania parasites in the blood, during and after different antileishmanial therapies, and to find predictors for clinical relapse of disease.MethodsData from three clinical trials, in which Eastern African VL patients received various antileishmanial regimens, were combined in this study. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during, and up to six months after treatment. An integrated population pharmacokinetic-pharmacodynamic model was developed using non-linear mixed effects modelling.ResultsParasite proliferation was best described by an exponential growth model, with an in vivo parasite doubling time of 7.8 days (RSE 12%). Parasite killing by fexinidazole, liposomal amphotericin B, sodium stibogluconate, and miltefosine was best described by linear models directly relating drug concentrations to the parasite elimination rate. After treatment, parasite growth was assumed to be suppressed by the host immune system, described by an Emax model driven by the time after treatment. No predictors for the high variability in onset and magnitude of the immune response could be identified. Model-based individual predictions of blood parasite load on Day 28 and Day 56 after start of treatment were predictive for clinical relapse of disease.ConclusionThis semi-mechanistic pharmacokinetic-pharmacodynamic model adequately captured the blood parasite dynamics during and after treatment, and revealed that high blood parasite loads on Day 28 and Day 56 after start of treatment are an early indication for VL relapse, which could be a useful biomarker to assess treatment efficacy of a treatment regimen in a clinical trial setting.

Among OECD countries in 2022, South Korea had the highest rate of yearly visits to a doctor per capita. On average, people in South Korea visited the doctors 15.7 times per year in person. Health care utilization is an important indicator of the success of a country’s health care system. There are many factors that affect health care utilization including healthcare structure and the supply of health care providers.

OECD health systems

Healthcare systems globally include a variety of tools for accessing healthcare, including private insurance based systems, like in the U.S., and universal systems, like in the U.K. Health systems have varying costs among the OECD countries. Worldwide, Europe has the highest expenditures for health as a proportion of the GDP. Among all OECD countries, The United States had the highest share of government spending on health care. Recent estimates of current per capita health expenditures showed the United States also had, by far, the highest per capita spending on health worldwide.

Supply of health providers

Globally, the country with the highest physician density is Cuba, although most other countries with high number of physicians to population was found in Europe. The number of graduates of medicine impacts the number of available physicians in countries. Among OECD countries, Latvia had the highest rate of graduates of medicine, which was almost twice the rate of the OECD average.