Employment rate of parents living with dependent children as a couple or lone parent by age of the youngest child in the UK.

The benchmark interest rate in the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

http://dx.doi.org/10.1093/pubmed/fdab017On March 11, the WHO declared that the novel SARS-CoV-2 virus was at pandemic levels. In the UK, a number of public health measures such as social distancing and lockdown were introduced to minimise viral transmission. We sought to assess whether or not the initial outbreak of COVID-19 was associated with a change in the prescription rates of ICS, prednisolone, and antibiotics in primary care in England.Find the datasets and R scripts used for each medication analysis separately to confirm our results.

Files for use with the R script accompanying the paper Cooper et al. (2019). Note that this script also uses files from https://doi.org/10.14466/CefasDataHub.34_ (details provided in script). Cooper, K.M., Bolam, S.G., Downie, A-L., Barry, J. (2019) Biological- based habitat classification approaches promote cost- efficient monitoring: An example using seabed assemblages. J. Appl. Ecol. 56, 1085–1098. https://doi. org/10.1111/1365-2664.13381

.. _https://doi.org/10.14466/cefasdatahub.34: https://doi.org/10.14466/CefasDataHub.34

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting at the local authority level in the United Kingdom.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United Kingdom.

This dataset provides annual estimates of species occupancy and species trend estimates in the form of growth rates for 5,293 UK invertebrate, bryophyte and lichen species for the period 1970 to 2015. Estimates are provided at the country level for England, Scotland, Wales and Northern Ireland as well as for the UK and Great Britain (GB) where possible. These data were generated using observations of species collated by UK recording schemes and societies as the input data for a Bayesian occupancy model. The outputs resulting from this modelling framework are presented in three forms: • 1000 samples from the modelled posterior distribution of the proportion of occupied sites for each species for each year and for each region analysed. • Summary tables from the model outputs detailing mean occupancy and associated statistics including credible intervals and rhat measure of convergence. • Derived species trend estimates in the form of annual percentage growth rates. Annual estimates derived from fine-grained data (1x1km squares) have not been determined for this set of species before, making this a unique dataset that broadens knowledge on UK biodiversity change. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

The Corporate Tax Rate in the United Kingdom stands at 25 percent. This dataset provides - United Kingdom Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The percentage of adults (aged 16 and over) that responded to the question "How often do you feel lonely?" with "Always or often" or "Some of the time"

Rationale At the beginning of 2018, the Prime Minister highlighted the issue of loneliness, announcing a Minister for Loneliness and committing to develop a national strategy to help tackle loneliness and a national measure for loneliness.

The national strategy, A Connected Society: A Strategy for Tackling Loneliness, was published on 15 October 2018. The commitments made by the Department of Health and Social Care (DHSC) and NHS England in the strategy identify loneliness to be a serious public health concern.

In keeping with the Loneliness Strategy, loneliness is defined here as: “a subjective, unwelcome feeling of lack or loss of companionship. It happens when we have a mismatch between the quantity and quality of social relationships that we have, and those that we want.” This is based on a definition first suggested by Perlman and Peplau in 1981(1).

Loneliness is a feeling that most people will experience at some point in their lives. When people feel lonely most or all of the time, it can have a serious impact on an individual’s well-being and their ability to function in society. Feeling lonely frequently is linked to early deaths and its health impact is thought to be on a par with other public health priorities like obesity or smoking.

Lonely people are more likely to be readmitted to hospital or have a longer stay. There is also evidence that lonely people are more likely to visit a General Practitioner or Accident and Emergency and more likely to enter local authority funded residential care.

At work, higher loneliness among employees is associated with poorer performance on tasks and in a team, while social interaction at work has been linked to increased productivity.

Loneliness can affect anyone of any age and background. It is important to measure loneliness because the evidence on loneliness is currently much more robust and extensive on loneliness in older people, but much less for other age groups including children and young people.

If more people measure loneliness in the same way, we will build a much better evidence base more quickly. That’s why the Prime Minister asked the Office for National Statistics (ONS) to develop national indicators of loneliness for people of all ages, suitable for use on major studies.

When reporting the prevalence of loneliness, ONS advise using the responses from the direct question, “How often do you feel lonely?” The inclusion of the direct loneliness measure in the Public Health Outcomes Framework (PHOF) will help inform and focus future work on loneliness at both a national and local level, providing a focus to support strategic leadership, policy decisions and service commissioning.

In this first set of data on loneliness prevalence at a local authority level, we have merged the two most frequent categories of feeling lonely (often or always and some of the time). This is due to small sample sizes and the limitations of this data will be explained in more detail in the caveats section.

This will be replaced next year by a 2-year pooled dataset which will have large enough sample sizes to report chronic loneliness. Presenting the data this year will help local authorities to work preventatively to tackle chronic loneliness by showing whether a local area has higher than national average levels of loneliness.

(1) Perlman D and Peplau LA (1981) 'Toward a Social Psychology of Loneliness', in Gilmour R and Duck S (eds.), Personal Relationships. 3, Personal Relationships in Disorder, London: Academic Press, pp. 31–56.

Definition of numerator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely" that answered "Always or often" or "Some of the time". Active Lives Adult Survey data is collected November to November.

Definition of denominator Weighted number of respondents aged 16 and over, with a valid response to the question "How often do you feel lonely?".Denominator values in the Download data are unweighted counts. All analyses for this indicator have been weighted to be representative of the population of England.Active Lives Adult Survey data is collected November to November.

Caveats

Due to the sample size at local authority level, the "often or always" category is merged with the next most severe category of loneliness (people who respond as feeling lonely “some of the time”).

Standard practice is to report the two categories separately. However, data from other sources shows a degree of volatility in the ratio between these categories at the local authority (LA) level.

Therefore, there is a risk that when two local authorities are both reported as having 25% of people feeling lonely (often or always combined with some of the time), the actual figures for "often or always" might differ significantly. For example, one LA might have 24% often and always while another has only 3%, which would not be apparent in the combined category.

This could lead to underestimation or overestimation of chronic loneliness levels by local authorities.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Daily Coronavirus (Covid-19) positive tests in Leicester City Council and surrounding districts.Data for the most recent 4-5 days is likely to be incomplete.Please note automatic updates to this dataset were discontinued on 12th December 2023.

Background: The current propagation models of COVID-19 are poorly consistent with existing epidemiological data and with evidence that the SARS-CoV-2 genome is mutating, for potential aggressive evolution of the disease.Objectives: We looked for fundamental variables that were missing from current analyses. Among them were regional climate heterogeneity, viral evolution processes versus founder effects, and large-scale virus containment measures.Methods: We challenged regional versus genetic evolution models of COVID-19 at a whole-population level, over 168,089 laboratory-confirmed SARS-CoV-2 infection cases in Italy, Spain, and Scandinavia at early time-points of the pandemic. Diffusion data in Germany, France, and the United Kingdom provided a validation dataset of 210,239 additional cases.Results: Mean doubling time of COVID-19 cases was 6.63 days in Northern versus 5.38 days in Southern Italy. Spain extended this trend of faster diffusion in Southern Europe, with a doubling time of 4.2 days. Slower doubling times were observed in Sweden (9.4 days), Finland (10.8 days), and Norway (12.95 days). COVID-19 doubling time in Germany (7.0 days), France (7.5 days), and the United Kingdom (7.2 days) supported the North/South gradient model. Clusters of SARS-CoV-2 mutations upon sequential diffusion were not found to clearly correlate with regional distribution dynamics.Conclusion: Acquisition of mutations upon SARS-CoV-2 spreading failed to explain regional diffusion heterogeneity at early pandemic times. Our findings indicate that COVID-19 transmission rates are rather associated with a sharp North/South climate gradient, with faster spreading in Southern regions. Thus, warmer climate conditions may not limit SARS-CoV-2 infectivity. Very cold regions may be better spared by recurrent courses of SARS-CoV-2 infection.