These datasets contain records of Queensland's geodetic survey control information. The database provides for the effective management of the geodetic survey control information for Queensland for which the Department of Resourcesis responsible under the Survey and Mapping Infrastructure Act 2003. The records contain: Registered number - number of survey control mark Local Authority - name of local authority Vertical Height - height of mark Vertical Datum - datum of the height.

This map service displays survey control information managed and maintained by the Department of Resources, Queensland. The service includes all survey control marks presented by datum and survey type, and identifies Continuously Operating Reference Station (CORS) network sites. Layers only display at effective scales from 1:250000 to aid legibility.

The Datum of the majority of the City of Sacramento benchmarks is the National Geodetic Vertical Datum of 1929 (NGVD 29) and was based upon a plane datum with reference to four U.S. Government monuments. Some North American Vertical Datum 1988 (NAVD 88) differential baselines have been conducted and the elevations of those benchmarks have been included and identified in this database.VERTICAL CONTROL DISCLAIMER AND GENERAL USE DISCLAIMER City of Sacramento Vertical Control Network, also known as “City of Sacramento Datum” information, is furnished by the Department of Public Works - Engineering Services. It was developed and collected for the purpose of establishing reference points for all survey activities within Sacramento City limits. City of Sacramento makes no warranties, expressed or implied, concerning the accuracy, completeness, reliability, or suitability of this data for any other particular use. Furthermore, the City of Sacramento assumes not liability for any errors, omissions, or inaccuracies associated with the use or misuse of such data. The City of Sacramento tries to keep this information current and accurate.If you find a missing or destroyed City of Sacramento benchmark please contact the City Land Surveyor by calling (916) 808-8777.

ACCEPTABLE USE The City of Sacramento Datum (unadjusted NGVD29) local benchmarks provided here are acceptable as City of Sacramento benchmarks for the purpose of establishing and extending vertical control to design surveys. Reference Sacramento City Code, §1.12.010.All information compiled on this website is provided as a public service and for general informational purposes only. In preparation of these pages, every effort has been made by the Department of Public Works - Engineering Services to offer the most current, correct and concise information possible. The City of Sacramento and its authorized agents and contractors disclaim any responsibility for typographical errors and accuracy of the information that may be contained on the City of Sacramento website; www.cityofsacramento.orgBy accessing the information, data, materials and links contained in the City of Sacramento World Wide Web pages, you hereby agree to and accept the following terms and conditions: The Department of Public Works - Engineering Services shall not be liable for the improper or incorrect use of data, information, materials, links or related graphics described and /or contained herein. The Department of Public Works - Engineering Services shall not be liable for any demand claim, regardless of form or action, arising out of or incident to the posting of information or data on this website; the accessing or use of any information or data on this website; and/or the acts or omissions of any person or entity accessing or using any information from this website.The user hereby recognizes that the information, data, materials and related graphics are dynamic and may change over time without notice. The Department of Public Works - Engineering Services is not responsible for the use or reliance upon this information. There are links and pointers to third party internet websites contained in the City of Sacramento website. These sites linked from the City of Sacramento website are not under the City’s control. The City of Sacramento and its authorized agents and contractors do not assume any responsibility or liability for any information, communications or materials available at such linked sites, or at any link contained in a liked site. Each individual site has its own set of policies about what information is appropriate for public access. User assumes sole responsibility for use of third party links and pointers.

This dataset provides information about the survey control network, position, position accuracy, mark name, mark type, condition and unique four letter code for geodetic marks in terms of New Zealand's official geodetic datum, New Zealand Geodetic Datum 2000 (NZGD2000).

The dataset only contains marks that are within the New Zealand mainland and offshore islands. These positions have been generated using geodetic observations such as precise differential GPS or electronic distance and theodolite angle measurements. The positions are either 2D or 3D depending of the availability of this measurement data.

The source data is from Land Information New Zealand's (LINZ) Landonline system where it is used by Land Surveyors. This dataset is updated daily to reflect changes made in the Landonline.

The mark network is segmented into six control networks which provide control marks for specific purposes. The control_network field within this layer records this network with the 3 or 4 letter abbreviation code as follows:

NRF - National Reference Frame
NDMN - National Deformation Monitoring Network
RDMN - Regional Deformation Monitoring Network
LDMN - Local Deformation Monitoring Network
CHN - Cadastral Horizontal Control Network
CVN - Cadastral Vertical Control Network
BGN - Basic Geospatial Network
NHN - National Height Network  

Note a geodetic mark can be in more than network. Also not all geodetic marks are currently associated with a geodetic network. Those that are may be referred to as a control mark. For more information about the control networks refer to http://www.linz.govt.nz/geodetic/geodetic-programme/survey-control-networks/.

Accuracy

Geodetic marks with a coordinate order of 5 or less have been positioned in terms of NZGD2000. Lower order marks (order 6 and greater) are derived from cadastral surveys, lower accuracy measurement techniques or inaccurate historical datum transformations, and may be significantly less accurate.

The accuracy of NZGD2000 coordinates is described by a series of 'orders' classifications. Positions in terms of NZGD2000 are described by three-dimensional coordinates (latitude, longitude, ellipsoidal height). The accuracy of a survey mark is indicated by its order. Orders are classifications based on the quality of the coordinate in relation to the datum and in relation to other surrounding marks. For more information see http://www.linz.govt.nz/geodetic/datums-projections-heights/heights/coordinate-orders/

Note that the accuracy applies at the time the mark was last surveyed. Refer to the web geodetic database for historical information about mark coordinates.

Note also that the existence of a mark in this dataset does not imply that there is currently a physical mark in the ground - the dataset includes destroyed or lost historical marks. The geodetic database provides more information on the mark status, valid at last time it was visited by LINZ or a maintenance contractor.

Survey Marks and their related Survey Plans administered under the Survey Act 1992 to provide a control network for land surveys underpinning the State's cadastre. The Historical GDA94 copies of data from this site contain GDA94 coordinate information and have not been updated since 1 December 2018.

Please sign-in (top-right) to Launch SCIMS Online your existing SIX login credentials will not workif you used SCIMS Online in the last 6 months you should have received a activation email from Okta. Please access our Information Sheet for further informationDCS Spatial Services is aware of an ongoing issue requiring users to click the login button multiple times to launch SCIMS. We are hoping to have this resolved shortly.

The Survey Control Information Management System (SCIMS) is a database that contains the coordinates, heights and related attributes for Permanent Survey Marks (PSMs) constituting the State Control Survey. SCIMS online is a tool which enables users to discover and download data related to each survey mark contained within SCIMS. This includes position, accuracy, source and all other technical information, required by surveyors, to fulfil their obligations under NSW legislation when undertaking surveys and creating survey plans.The NSW Survey Mark app allows users to search and view the location of any permanent survey marks across the state, access mark details or report a change in its status.To download the NSW Survey Mark Android app, please visit Google Play.To download the NSW Survey Mark iPhone app, please visit the iTunes Store.MetadataContent TitleSCIMS OnlineContent TypeWeb ApplicationDescriptionSCIMS online is a toll which enables users to discover and download data related to each survey mark contained within the Survey Control Information Management System (SCIMS).Initial Publication Date15/11/2023Data Currency15/11/2023Data Update FrequencyOtherContent SourceWebsite URLFile TypeDocumentAttributionData Theme, Classification or Relationship to other DatasetsAccuracySpatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:4326WGS84 Equivalent ToGDA94Spatial ExtentContent LineageData ClassificationUnclassifiedData Access PolicyOpenData QualityTerms and ConditionsCreative CommonsStandard and SpecificationData CustodianDCS Spatial Services346 Panorama AveBathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorData DistributorAdditional Supporting InformationTRIM Number

Statewide dataset containing the location of geodetic control points and their associated attributes. For attribute data dictionary, see the file Geodetic-CodeGuide.doc.

Export Data Access API

This Archaeological Survey of Ireland dataset is published from the database of the National Monuments Service Sites and Monuments Record (SMR). This dataset also can be viewed and interrogated through the online Historic Environment Viewer. A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included. The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. https://creativecommons.org/licenses/by/4.0/ Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland. This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset. Open Data Bulk Data Downloads (version date: 23/06/2023) The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. For a live and current view of the data, please use the web services or the query tool in the Historic Environment Viewer. ESRI Shapefiles of the SMR points and SMRZone polygons are also available. The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-tier, proportionately stratified random sample was used. In the first stage, the settlements (sampling points) were randomly selected so that the composition of the settlements in the sample, according to several variables such as population size, infrastructure, etc., follows the composition of the entire territory of Hungary.

In the second stage the number of respondents to be interviewed in each settlement was determined proportionate to the population size of the settlement. Eighty settlements were used as sampling points: Budapest as one with its 23 districts and 79 rural settlements.

The sample represents the settlement network of Hungary. The composition of the persons in the sample according to sex, age and residence is identical with the composition of the population above the age of 18.

As a sampling frame Szonda Ipsos used the address database produced by the National Office for Censuses, which is a quarterly up-dated entire electronic registry of all Hungarian persons residing in the country.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

NSW Positioning Theme Survey Mark GDA94

Note: GDA94 coordinates for NSW survey marks are no longer maintained. This data has been superseded by NSW Positioning Theme Survey Mark GDA2020 multiCRS or NSW Positioning Theme - Survey Mark GDA2020.

Please Note:
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments.
A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.
In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.

Please note that SCIMS Online is the official option for obtaining state control survey data for cadastral surveys as it offers legal traceability. SCIMS online is accessible via the Spatial portal.

2020 DATABASE (Excel): The joint federal-provincial Regional Geochemical Surveys (RGS) have been carried out in British Columbia since 1976 as part of the National Geochemical Reconnaissance (NGR) program to aid exploration and development of mineral resources. The British Columbia Geological Survey (BCGS) maintains provincial geochemical databases capturing information from multi-media surveys. This 2020 release of the most current and complete province-wide geochemical data set collected under the (RGS) program. The database was compiled from 116 original sources with 65,429 samples and about 5 million determinations analyzed using 18 methods in 18 laboratories. This release augments the database with new RGS data compiled from BCGS and Geoscience BC publications between 2016 and 2019. Compared with the data in the last release, data in this release have been given further quality control treatment and revision. For the ease of use and consistency with previously published data, the data set was generated from the RGS database in a flat tabular format. The 2020 data set, released as BCGS GeoGile 2020-08, is presented in two MS Excel files, ‘RGS2020_ data.xlsx’ and ‘RGS2020_metadata.xlsx’. The data tables capture locations, field observations, analytical results and laboratories, and geology underlying sample sites for stream-, lake- and moss-sediment, water and lake samples, heavy mineral concentrates, tree twig, and needle ash. The analytical determinations include up to 63 analytes from sediment samples and up to 78 analytes from water samples. These samples, collected at an average density of about 1 site per 7–13 km2, provide representative geochemical data for the catchment basin upstream from the sample site. The RGS currently covers approximately 80 percent of the province.

All credit for variables in AHRQ_included_variables.csv is attributed to

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  Human Services, Health Resources and Services Administration, Bureau of Health Workforce,
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• AHRQ SDOH Database (v1), from American Hospital Association Annual Survey Data,
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• AHRQ SDOH Database (v1), from Health Resources and Services Administration. (2021, Nov 1).
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• AHRQ SDOH Database (v1), from the Foundation for AIDS Research. Opioid & Health Indicators
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• AHRQ SDOH Database (v1), from United States Census Bureau. (n.d.). County Adjacency File
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• AHRQ SDOH Database (v1), from United States Census Bureau. County Business Patterns [Data
  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
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• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
  2021 from https://nces.ed.gov/ccd/files.asp#Fiscal:1,LevelId:5,Page:1.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, Division for Heart
  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
  http://nccd.cdc.gov/DHDSPAtlas.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention. NCHHSTP
  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
• AHRQ SDOH Database (v1), from PLACES: Local Data for Better Health. Center for Disease
  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
  https://chronicdata.cdc.gov/browse?category=500+Cities+%26+Places&sortBy=newest&utf8.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, National Center for
  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
  Data are from the Multiple Cause of Death Files, 1999–2019, as compiled from data provided by
  the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
  http://wonder.cdc.gov/mcd-icd10.html.
• United States Census Bureau. Cartographic Boundary Files – Shapefile.
  https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
  https://www.countyhealthrankings.org.
• AHRQ SDOH Database (v1), from U.S. Department of Education. (n.d.). Civil Rights Data
  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
• AHRQ SDOH Database (v1), from Community Resilience Estimates Social, Economic, and
  Housing Statistics Division U.S. Census Bureau. Retrieved September 2021 from
  https://www.census.gov/programs-surveys/community-resilience-estimates.html.
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
  Quality Data. Air Quality Statistics Report. Retrieved March 2022 from:
  https://www.epa.gov/outdoor-air-quality-data/air-quality-statistics-report
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
• AHRQ SDOH Database (v1), from Economic Research Service (ERS), U.S. Department of
  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
  https://www.ers.usda.gov/data-products/food-access-research-atlas/
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  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
  https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-anddocumentation-downloads/.
• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Home Health
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• AHRQ SDOH Database (v1), from the Department of Homeland Security. Homeland Infrastructure
  Foundation-Level Data (HIFLD)—Urgent Care Facilities. 2018. Available: https://hifldgeoplatform.opendata.arcgis.com/datasets/urgent-care-facilities.
• AHRQ SDOH Database (v1), from the Indian Health Service. IHS facilities. 2021. Available:
  https://www.ihs.gov/locations/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
  State/County Penetration, from https://www.cms.gov/Research-Statistics-DataandSystems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-CountyPenetration.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June).
  Medicare Geographic Variation Public Use File, from https://www.cms.gov/Research-StatisticsData-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention; U.S.
  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
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  Health. (2020). Medicare Mapping Disparities Tool, from https://data.cms.gov/tools/mappingmedicare-disparities-by-population.
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  Medically Underserved Areas, from https://data.hrsa.gov/data/download.
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  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
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  Home Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C.,
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  U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.
  Retrieved Oct 29, 2021, from https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-
  0248.1.xml.
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  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
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  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
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  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
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  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
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  Jang, H., &

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Geographic coverage

15 federal states: Distrito Federal, Guanajuato, Jalisco, Estado de México, Michoacán, Qurétaro, Guerrero, Oaxaca, Puebla, Veracruz, Yucatán, Chihuahua, Nuevo León, San Luis Potosí, Sonora

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample used was a probabilistic, multistage, stratified and clustered sample and represented urban and rural strata.

Mexico has 32 Federal States, which were divided, into 3 regions: Centre, South and North. Out of these regions, 15 were selected as follows: Centre: Distrito Federal, Guanajuato, Jalisco, Estado de México, Michoacán, Qurétaro South: Guerrero, Oaxaca, Puebla, Veracruz, Yucatán North: Chihuahua, Nuevo León, San Luis Potosí, Sonora

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Scottish Marine and Freshwater Science Vol 8 No 16 Monitoring and assessment of fish communities is essential to demonstrating the achievement of GES across the waters of the Northeast Atlantic covered by the MSFD. Coastal MSs bordering the Northeast Atlantic have invariably nominated their groundfish surveys as part of their monitoring programmes to supply the data necessary to derive the indicators that will be used to assess the state of fish communities, both within their national waters and across the whole Northeast Atlantic region. Data obtained by these groundfish surveys are, for the most part, freely available for download from the DATRAS database portal on the ICES website. Data are initially checked by national data centres prior to submission to ICES and a further screening process is applied at ICES before the data are accepted and incorporated into the DATRAS database. However, the DATRAS screening process was implemented in 2009 only for data from 2004 onwards. Some survey time-series extend back to the 1960s and more historic data may not have been subject to the same level of quality control as these more recent data. Data were initially collected to address fisheries management needs, which primarily focused attention on commercial species. Gradual adoption of an ecosystem approach to management has raised the importance of non-target species, in order to facilitate the development and application of ecological indicators for the broader fish community. Thus the type of information collected, the level of detail and resolution in the data, has gradually evolved over time. These historic changes in groundfish survey practices have left a quality assurance legacy in the data that needs to be addressed to ensure that the groundfish survey monitoring programmes are fully fit to meet modern day needs of the MSFD. Historically the surveys operated by different countries have followed their own particular sets of protocols and practices. The procedure for uploading data to DATRAS has tended to preserve these national differences in data format, and this has necessitated the inclusion in DATRAS of numerous additional fields, each informing as to how other fields in the database should be interpreted. Here 19 surveys involving a dozen different countries are examined. In order to derive a single format, quality assured monitoring programme data product covering the entire Northeast Atlantic region, these national, inter-survey, inconsistencies all need resolution. In many instances, particularly in the more historic data, key information is either absent or incorrect, and these missing or erroneous values need replacement by modelled estimates. This document describes the process by which these issues were all resolved to derive 19 separate consistent and fully quality assured survey data products. These data products constitute a unified monitoring programme covering the continental shelf waters of the Northeast Atlantic, from northern Norway to Gibraltar, which can facilitate assessment of the state of fish communities across this whole region. Each survey is first described and a brief history provided. A strict error checking protocol was developed and applied to data in all key fields used in deriving the survey data products. Details of each of these are provided in this document. Detailed descriptions are provided of the two types of file that make up each individual survey data product: "Sampling Information" and "Biological Information".

Abstract

The UN Joint Programme focused on Rural Women’s Economic Empowerment (UNJP-RWEE) was launched in Ethiopia in 2014 by UN Women, the Food and Agriculture Organization of the UN (FAO), the World Food Programme (WFP), and the International Fund for Agriculture Development (IFAD). UNJP-RWEE was a five-year long initiative with the objective of accelerating the economic empowerment of rural women in the regions of Oroma and Afar. The project provided women with greater access to credit through women-run rural savings and credit cooperatives (RUSACCOs), as well as numeracy, literacy, finance, and business-development training; agricultural livestock and technology transfers; agricultural training; and community-run educational conversations in healthy eating choices and nutrition. To assess the extent to which the UNJP was effective in empowering women economically, an impact evaluation was conducted by the FAO in partnership with IFAD, and IFPRI. The FAO received a grant from GAAP2-IFPRI, facilitated by the Gates Foundation, to conduct a quasi-experimental impact evaluation with a difference-in-difference approach using a revised version of the Women’s Empowerment in Agriculture Index (WEAI), the Pro-WEAI. In Oromia, the list of beneficiaries including their Kebeles were retrieved from the baseline survey. In total 750 households were surveyed in the Oromia region. In Afar, the number of beneficiary households interviewed increased to 450, 250 of which were beneficiaries and the rest control. In addition to the 95 beneficiary household included at baseline, 155 new beneficiaries were included. Two additional control Kebeles were also included in Afar. Follow-up interviews were conducted with 389 women in the beneficiary communities and 358 women in the comparison communities, and 303 men in the beneficiary households and 314 men in the comparison communities. In all, there are 736 households where the same female respondent was administered the survey at both baseline and end-line.

Geographic coverage

Regional Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

In the Oromia National Regional State, the same Woredas that were covered in the baseline are maintained. The list of project beneficiaries including their Kebeles was retrieved from the baseline survey which was conducted in 2017. In addition, control Kebeles were also obtained from the previous survey database. In the baseline, in the beneficiary communities, a random sample was drawn from the RUSSACO members, and from comparable kebeles. Six beneficiary kebeles were selected in Oromia: (1) Illuf Dirre and (2) Nannoo Chemerri in the Yaya Gulele Woreda; (3) Bura Adelle and (4) Wabe Burkitu in the Dodola Woreda; and (5) Abine Garmamme and (6) Annenno Shisho in the Adami Tulu Woreda. The comparison kebeles are adjacent communities in which the UNJP-RWEE does not operate but that are similar in size; have similar agricultural systems, livelihoods; and cultural norms, and thus are deemed valid counterfactuals. In Oromia, the control communities are: (1) Lemi; (2) Dedfe; (3) Haleko Gulenta Boke; (4) Werji Washingula; (5) Baressa; and (6) Keta Berenda. In Afar National Regional State, in addition to households interviewed at baseline, an additional 150 beneficiary households were interviewed in: (1) Asboda and (2) Boyina in the Dubti Woreda, and an additional 50 control households were interviewed from a newly selected Kebeles. The control communities were therefore (1) Hanikesen and (2) Aredo; (3) Gudmaydil; and (4) Gayder).

Mode of data collection

Computer Assisted Personal Interview [capi]

All wastewater monitoring programs assess the environmental situation in the discharge area by comparing it to the situation where there is no discharge. Thus the selection of the reference or control stations is of critical importance in evaluating effects: Control stations must be as similar as possible to the area being studied except that they are not exposed to outfall materials. The Project has encountered the problem of selecting proper control sites in designing its surveys of southern California discharge areas. We considered using sites off the Channel Islands but rejected them because they are not oceanographically or biologically comparable to mainland sites. We also looked at data from the control sites used in the past by various dischargers and found that some of these were in areas influenced' by the outfalls being investigated and consequently gave invalid reference information. In some monitoring surveys, a single reference station and a single set of measurements have been used as the basis for determining the natural or background conditions for an outfall site. Yet careful analysis of many samples reveals that chemical and biological conditions vary with water depth and type of bottom material and can differ by as much as a factor of 10, even in areas not influenced by man. Plainly, no single control station or set of measurements is valid for reference purposes. Numerous benthic monitoring surveys, designed to assess the effects of existing or future discharges, have been taken on the southern California mainland shelf since 1956. While such surveys have provided much new information on the abundance and diversity of thousands of species of bottom organisms and fishes, and on the levels of pollutants in sediments around outfalls, few have provided a data base that would permit the identification of control sites, or areas where background conditions exist. An outstanding exception is the mainland shelf survey produced by the California State Water Resources Control Board and conducted some 20 years ago by the Allan Hancock Foundation, University of Southern California (California State Water Resources Control Board 1967; Jones 1969). That survey provided useful information on conditions at that time; however, we do not know if the results are representative today. Moreover, the previous survey did not provide data on sediment pollutant concentrations or trawl-caught fish and invertebrate populations--two types of information that are needed at present, particularly for rural areas of this coast. Accordingly, in 1977, the Coastal Water Research Project sponsored a new survey of the mainland shelf from Point Conception to the United States/Mexico border. The goal of the survey was to identify possible control areas for contrasting with existing municipal wastewater discharge sites and to define the apparent normal variations in the chemistry and biology of the fine sediment areas covering much of the mainland shelf.