This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series. • COVID-19 and the production of statistics Due to the coronavirus illness (COVID-19) disruption, the quality and coverage of some of our statistics has been affected, for example by an increase in non-submissions for some datasets. We are also seeing some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period. • Update on previously unpublished measures 72 hour follow-up measures (MHS78, MHS79 and MHS80) have now been released for Performance October 2020 onwards. Data for these measures for previous reporting periods will be published as soon as they are available. NHS Digital apologises for the inconvenience caused. • Early release of statistics To support the ongoing COVID-19 work, Provisional January 2021 monthly statistics were made available early and presented on our supplementary information pages. https://digital.nhs.uk/data-and-information/supplementary-information/2021/provisional-january-2021-mental-health-statistics • Changing existing measures The move to MHSDS version 4.1 from April 2020 has brought with it changes to the dataset; the construction of a number of measures have been changed as a result. From Performance August 2020 onwards, the methodology for calculating restrictive interventions (MHS76 and MHS77) in the reporting month has been updated to include all restraints that span several months. Previously the measure only includes restraints that started or ended in the month and did not include those spanning more than 2 months. This change predominately impacts segregation. From Performance September 2020 onwards, the methodology for MHS26 (Delayed transfer of care) has been updated. Previously only the first midnight of a delayed transfer of care within a hospital provider spell was excluded. The updated methodology now excludes the first midnight for delay start date where there is a gap of more than one day within the same hospital provider spell. This is to take into account valid gaps between delay reasons. Full details of these changes are available in the associated Metadata file. From Performance September 2020 onwards, a presentational change has been introduced to restrictive interventions csv files to exclude providers with no inpatients. • New measures A number of new measures have been included from Performance July 2020 onwards: MHS81 Number of Detentions MHS82 Number of Short Term Orders MHS83 Number of uses of Section 136 MHS84 Number of Community Treatment Orders Full details of these are available in the associated Metadata file.

This table contains data on traffic performance (vehicle kilometres) and the number of vehicles of Dutch delivery vans divided by age of the vehicle, weight and fuel type. The vehicle population for which mileage is estimated is based on motor vehicle fleet statistics. The population of the figures in this table is based on the old motor vehicle fleet selection method. The difference between the old and the new selection method is described in a method report, see section 4. The series of kilometers estimated on the basis of the old vehicle population runs up to and including reporting year 2020. The series based on the new population is available from of the 2018 reporting year. The way in which the kilometers are estimated has not changed, only the population. The figures for the 2020 reporting year have been corrected for the smoothing effect of the method by means of a correction factor. This smoothing effect smoothes out the annual variation in the figures. This gives a distorted picture of periods in which mobility suddenly changes radically, such as in 2020 as a result of the corona crisis. Data available from: 2015 up to and including 2020 Status of the figures: The figures in this table for 2015 up to and including 2019 are final and those for 2020 have a provisional status. Changes as of November 10, 2022: None, this table has been discontinued. This table is followed by the Traffic performance vans table; weight, age, fuel, see section 3. When will there be new figures? Not applicable anymore.

This table contains data on the number of trucks and semi-trailer tractors (freight vehicles) in use in the name of companies, traffic performance (miles driven) of these vehicles and average annual mileage. These figures are divided by industry (SBI 2008), age, loading capacity and environmental class of the vehicle and size of the company. The figures relate to freight vehicles with a valid Dutch license plate that were admitted to traffic on public roads during (part of) the reporting year, including: - freight vehicles that belong to the company stock in (part of) the year. - freight vehicles that are only active on the road network for part of the year, such as new or imported vehicles and vehicles that have been scrapped or exported during the year. The calculated numbers are higher than the number of registered (active) trucks and semi-trailer tractors on the reference date January 1, as published in the tables on the motor vehicle fleet. Data available from: 2009 to 2020 Status of the figures: The figures for 2018, 2019 and 2020 are provisional. The figures for previous years are final. This table uses multiple (external) source files. Due to changes in these files, (final) figures in this table may be adjusted retroactively. Changes as of September 7, 2023: None, this table has been discontinued. This table is followed by Traffic performance of freight vehicles; kilometers, industries. See section 3. When will new figures be available? Does not apply

This publication covers annual estimates for waste collected by local authorities in England and the regions. These statistics are based on data submitted by all local authorities in England to WasteDataFlow on the waste they collect and manage.

The methodology and recycling explainer documents give background and context to this statistical notice, accompanying datasets and the waste and recycling measures they present.

There is also a further historical note on the definition of local authority collected waste relating to earlier releases.

The entire raw dataset is available in CSV format and can be found here: https://www.data.gov.uk/dataset/0e0c12d8-24f6-461f-b4bc-f6d6a5bf2de5/wastedataflow-local-authority-waste-management" class="govuk-link">WasteDataFlow - Local Authority waste management - data.gov.uk

Historic releases

https://webarchive.nationalarchives.gov.uk/ukgwa/20250102235615/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2022-2023

https://webarchive.nationalarchives.gov.uk/ukgwa/20230802024231/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results-202122" class="govuk-link">2021- 2022

https://webarchive.nationalarchives.gov.uk/ukgwa/20220503105415/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2020 - 2021

https://webarchive.nationalarchives.gov.uk/ukgwa/20210728220801/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2019-2020

https://webarchive.nationalarchives.gov.uk/ukgwa/20200604042448/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2018 - 2019

https://webarchive.nationalarchives.gov.uk/ukgwa/20190903035029/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2017 - 2018

https://webarchive.nationalarchives.gov.uk/ukgwa/20181207030346/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2016 - 2017

https://webarchive.nationalarchives.gov.uk/ukgwa/20170418015547/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2015 - 2016 This includes the ad hoc release entitled “Provisional 2016/17 local authority data on waste collection and treatment for England (April to June and July to September 2016)”.

https://webarchive.nationalarchives.gov.uk/ukgwa/20160512131028/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2014 - 2015

https://webarchive.nationalarchives.gov.uk/ukgwa/20150401112814/https://www.gov.uk/government/statistics/local-authority-collected-waste-management-annual-results" class="govuk-link">2013 - 2014

https://webarchive.nationalarchives.gov.uk/ukgwa/20140321171631/https://www.gov.uk/government/publications/local-authority-collected-waste-management-annual-results" class="govuk-link">2012 - 2013

Historic datasets

Historic datasets

Hualien County Local Taxation Bureau..............

Tables are presented listing parameters and fit statistics for 25,453 maximum likelihood logistic regression (MLLR) models describing hydrological drought probabilities at 324 gaged locations on rivers and streams in the Delaware River Basin (DRB). Data from previous months are used to estimate chance of hydrological drought during future summer months. Models containing 1 explanatory variable use monthly mean daily streamflow data (DV) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV from the previous 11 months. Outcomes are estimated 1 to 12 months ahead of their occurrence. Models containing 2 explanatory variables use monthly mean daily streamflow data (DV) and monthly mean precipitation data (P) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV and monthly mean P from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 3 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), and monthly mean maximum daily air temperature (T) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, and monthly mean maximum T from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Models containing 4 explanatory variables use monthly mean daily streamflow data (DV), monthly mean precipitation data (P), monthly mean maximum daily air temperature (T), and monthly mean potential evapotranspiration data (PET) to provide hydrological drought streamflow probabilities for July, August, and September as functions of monthly mean DV, monthly mean P, monthly mean maximum T, and monthly mean PET from the previous October, November, December, January, and February. Outcomes are estimated 5 to 12 months ahead of their occurrence. Explanatory variable selections for multiparameter models were optimized using random forest statistical methods. Selected single-parameter and multi-parameter models are provided. Overall correct classification rates tend to improve and models become more complex as the number of model explanatory variables increases from 1 to 4. Parameters for models with 1 explanatory variable are listed in the table labeled: “DRB-1_Variable_Equations.” Parameters for models with 2 explanatory variable are listed in the table labeled: “DRB-2_Variable_Equations.” Parameters for models with 3 explanatory variable are listed in the table labeled: “DRB-3_Variable_Equations.” Parameters for models with 4 explanatory variable are listed in the table labeled: “DRB-4_Variable_Equations.” Parameters describing models containing 1 explanatory variable may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s). Parameters describing models containing 2 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day). Parameters describing models containing 3 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P+ β3• T)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, β3 is a slope parameter DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day), T is a factor variable describing monthly mean maximum daily air temperature (degrees F). Parameters describing models containing 4 explanatory variables may be used to populate drought probability equations as follows: p =1/[1 + e^-(β0+ β1• DV+ β2• P+ β3• T+ β4• PET)] where: e is the base of the natural logarithm, β0 is an intercept parameter, β1 is a slope parameter, β2 is a slope parameter, β3 is a slope parameter, β4 is a slope parameter, DV is a factor variable describing monthly mean daily streamflow (ft3/s), P is a factor variable describing monthly mean precipitation (in/day), T is a factor variable describing monthly mean maximum daily air temperature (degrees F), PET is a factor variable describing monthly mean potential evapotranspiration (in/day). DV data span the period of record at each gage, ranging from July 1, 1899 through July 31, 2018. P, T, and PET data span the period associated with each gage beginning July 1, 1981 and ending July 31, 2018. Equation goodness of fit parameters document model strength, identifying the utility of each relation. Receiver Operating Characteristic (ROC) AUC values, scaled from 0 to 1, identify each model’s overall correct classification rate and are listed in the table labeled: “DRB-AUC_TABLE.” MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. Models with strong correct classification rates are provided for streams throughout the Delaware River Basin. Hydrological drought MLLR probability estimates inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making. More details of methods used may be found in: Austin, S.H., and Nelms, D.L., 2017, Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions: Journal of the American Water Resources Association, v. 53, p. 1133–1146, accessed November, 15, 2018, at https://doi.org/10.1111/1752- 1688.12562.