Previously known as BESS. The data sets are summarized by meaningful Healthcare Common Procedure Coding Current Procedural Terminology, HCPC CPT, code ranges. Brief descriptions for the code ranges and modifiers are provided in the readme file. The data set name contains the year followed by a five character sequence that is the HCPC CPT code. This HCPC CPT code corresponds to the first HCPC CPT in the selected code range of disciplines. Within each code range are, procedural, condition, or description subheadings. Each data set displays the allowed services, allowed charges, and payment amounts by HCPC CPT codes and prominent modifiers. These reports will only illustrate the modifiers where duplicative claim submissions occur. This is to avoid duplicate counting of services. Utilization for modifiers not affected by duplicative counting are collapsed into the Other category on the reports. Therefore, not all Centers for Medicare and Medicaid Services published modifiers are illustrated. The file is updated annually and usually available by September for the previous year.

This data collection contains summary statistics on population and housing subjects derived from questions on the 2010 Census questionnaire. Population counts for the total population and for the population 18 years and over are presented in four tables: (1) population of all ages by race, (2) population 18 years and over by race, (3) Hispanic or Latino population of all ages and not Hispanic or Latino population of all ages by race, and (4) Hispanic or Latino population ages 18 years and over and not Hispanic or Latino population ages 18 years and by race. A fifth table shows the number of occupied and vacant housing units. With one variable per table cell, plus additional variables with geographic information, the collection comprises three data files. The tables are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature): the United States as whole, states, regions, divisions, and other geographic areas that cross state boundaries, such as American Indian areas, metropolitan statistical areas, and micropolitan statistical areas. Tabulations for within-state summary levels down to the block level are contained in a companion data collection, Census of Population and Housing, 2010 [United States]: Redistricting Data (Public Law 94-171) Summary File (ICPSR 33441). Two ZIP archives constitute the collection. The first archive contains the data files, while the second contains the codebook and other documentation files, a Microsoft Access database shell, and SAS setups.

Abstract

Master Beneficiary Summary Files (MBSF)

Usage

This dataset page includes some of the tables from the Medicare Data in PHS's possession. Other Medicare tables are included on other dataset pages on the PHS Data Portal. Depending upon your research question and your DUA with CMS, you may only need tables from a subset of the Medicare dataset pages, or you may need tables from all of them.

The location of each of the Medicare tables (i.e. a chart of which tables are included in each Medicare dataset page) is shown here.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

Metadata access is required to view this section.

Section 2

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Usage Notes

Metadata access is required to view this section.

This data collection contains summary statistics on population and housing subjects derived from the responses to the 2010 Census questionnaire. Population items include sex, age, average household size, household type, and relationship to householder such as nonrelative or child. Housing items include tenure (whether a housing unit is owner-occupied or renter-occupied), age of householder, and household size for occupied housing units. Selected aggregates and medians also are provided. The summary statistics are presented in 71 tables, which are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan/micropolitan areas, counties, county subdivisions, places, ZIP Code Tabulation Areas (ZCTAs), school districts, census tracts, American Indian and Alaska Native areas, tribal subdivisions, and Hawaiian home lands. There are 10 population tables shown down to the county level and 47 population tables and 14 housing tables shown down to the census tract level. Every table cell is represented by a separate variable in the data. Each table is iterated for up to 330 population groups, which are called "characteristic iterations" in the Census Bureau's nomenclature: the total population, 74 race categories, 114 American Indian and Alaska Native categories, 47 Asian categories, 43 Native Hawaiian and Other Pacific Islander categories, and 51 Hispanic/not Hispanic groups. Moreover, the tables for some large summary areas (e.g., regions, divisions, and states) are iterated for portions of geographic areas ("geographic components" in the Census Bureau's nomenclature) such as metropolitan/micropolitan statistical areas and the principal cities of metropolitan statistical areas. The collection has a separate set of files for every state, the District of Columbia, Puerto Rico, and the National File. Each file set has 11 data files per characteristic iteration, a data file with geographic variables called the "geographic header file," and a documentation file called the "packing list" with information about the files in the file set. Altogether, the 53 file sets have 110,416 data files and 53 packing list files. Each file set is compressed in a separate ZIP archive (Datasets 1-56, 72, and 99). Another ZIP archive (Dataset 100) contains a Microsoft Access database shell and additional documentation files besides the codebook. The National File (Dataset 99) constitutes the National Update for Summary File 2. The National Update added summary levels for the United States as a whole, regions, divisions, and geographic areas that cross state lines such as Core Based Statistical Areas.

The 2010 Census National Summary File of Redistricting Data provides population counts for all persons and for persons 18 years and over by race (63 categories) and by Hispanic or Latino origin, as well as counts of all persons and persons 18 years and over that are not Hispanic/Latino cross-tabulated by race (63 categories). It provides the total housing unit counts and the counts of occupied and vacant units.The National Summary File of Redistricting Data is an extract of selected geographic areas (e.g., states, Congressional districts, and state legislative districts) previously released in the 2010 Census Redistricting Data (Public Law 94-171) Summary Files. In addition, this product provides summaries for the United States, regions, divisions, and other geographic areas that cross state boundaries, such as American Indian areas, metropolitan statistical areas, and micropolitan statistical areas.

In accordance with the Personal Data Protection Act, this agency shall provide public access to the names of personal data files and their related field names.

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Summary File 1 (SF 1) contains detailed tables focusing on age, sex, households, families, and housing units. These tables provide in-depth figures by race and Hispanic origin> some tables are repeated for each of nine race/Latino groups. Counts also are provided for over forty American Indian and Alaska Native tribes and for groups within race categories. The race categories include eighteen Asian groups and twelve Native Hawaiian and Other Pacific Islander groups. Counts of persons of Hispanic origin by country of origin (twenty-eight groups) are also shown. Summary File 1 presents data for the United States, the 50 states, and the District of Columbia in a hierarchical sequence down to the block level for many tabulations, but only to the census tract level for others. Summaries are included for other geographic areas such as ZIP Code Tabulation Areas (ZCTAs) and Congressional districts. Geographic coverage for Puerto Rico is comparable to the 50 states. Data are presented in a hierarchical sequence down the block level for many tabulations, but only to the census tract level for others. Geographic areas include barrios, barrios-pueblo, subbarrios, places, census tracts, block groups, and blocks. Summaries also are included for other geographic areas such as ZIP Code Tabulation Areas (ZCTAs).

To use the document, click on the link above and scroll to the link on the page titled “View the January 2024 Drug Tariff online”. Once in the file, use the search function on the left-hand side to search for ‘contraceptive’. Click on the link(s) that appear, and you will be able to see a breakdown of the products available and the cost in pence to the NHS. Data on prescribing volumes and costs for English dispensing in the community can be found in the published Prescription Cost Analysis statistics (PCA), this data is classified against what is considered to be the 'main' therapeutic use for the pharmaceutical 'presentation' expressed using the (pseudo) British National Formulary (BNF) hierarchy. The tables include a breakdown with each pharmaceutical presentation reported separately. https://www.nhsbsa.nhs.uk/statistical-collections/prescription-cost-analysis-england To use the document, click on the link above and scroll to the “Resource List”. From here you can choose to view the data on a national scale or narrowed down to local ICB level. Once in the file, we recommend downloading the file in order to filter the columns for ease of access. We recommend using the National Summary Tables and viewing Table 6: BNF presentation level data. Within this file you can filter by name in column C or the measurement in column D. We recommend that you access this data knowing the names of the medication, devices or patches you require. Please be aware of the PCA methodology which may be relevant depending on interpretation. https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/pca/pca_background_info_methodology_v001.html The basis of the cost information included in the PCA data is described in the 'metadata' section included with the release and relates to the amount included in the reimbursement. NHSBSA prescription data only covers prescription items that have been submitted for reimbursement by community NHS dispensing contractors - this does not include any direct purchases made by other parts of the NHS (for example hospitals or other facilities that are operated by NHS Trusts). The NHS Business Services Authority does not hold data - including cost information - about appointments.

Summary Trend TablesThe HCUP Summary Trend Tables include information on hospital utilization derived from the HCUP State Inpatient Databases (SID), State Emergency Department Databases (SEDD), National Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). State statistics are displayed by discharge month and national and regional statistics are displayed by discharge quarter. Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD.The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics:Overview of trends in inpatient and emergency department utilizationAll inpatient encounter typesInpatient encounter typeNormal newbornsDeliveriesNon-elective inpatient stays, admitted through the EDNon-elective inpatient stays, not admitted through the EDElective inpatient staysInpatient service lineMaternal and neonatal conditionsMental health and substance use disordersInjuriesSurgeriesOther medical conditionsED treat-and-release visitsDescription of the data source, methodology, and clinical criteria (Excel file, 43 KB)Change log (Excel file, 65 KB)For each type of inpatient stay, there is an Excel file for the number of discharges, the percent of discharges, the average length of stay, the in-hospital mortality rate per 100 discharges,1 and the population-based rate per 100,000 population.2 Each Excel file contains State-specific, region-specific, and national statistics. For most files, trends begin in January 2017. Also included in each Excel file is a description of the HCUP databases and methodology.

The Risk Management Agency (RMA) Summary of Business includes a variety of reports, data files, and an application that provide insurance experience for commodities grown and insured. This includes the most current information, some national reports, and the ability to create ad-hoc queries. Data for the past five years, which is updated each Monday, includes all of the business data that has been validated and accepted throughout the previous week with a cutoff every Friday. Data for the older years is static and no longer updated.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

The National Mortgage Database (NMDB®) is a nationally​​ representative five percent sample of residential mortgages in the United States. Publication ​of aggregate data from NMDB is a step toward implementing the statutory requirements of section 1324(c) of the Federal Housing Enterprises Financial Safety and Soundness Act of 1992, as amended by the Housing and Economic Recovery Act of 2008. The statute requires FHFA to conduct a monthly mortgage market survey to collect data on the characteristics of individual mortgages, both Enterprise and​ non-Enterprise, and to make the data a​vailable to the public while protecting the privacy of the borrowers.​Notes:1) All CSV file headers are now standardized as described in the Data Dictionary and Technical Notes and all CSV files are zipped.2) Alternate wide format CSV files are available. The wide format may be more easily opened by MS Excel.

The National Geophysical Data Center (NGDC) of NOAA, in cooperation with the National Geodetic Survey of NOAA, have published a Gravity CD-ROM containing observed and derived gravity measurements. Contributions to this data compilation include many national and international organizations, in both academia and government. The compact disc contains almost 620 Mbytes of data, partitioned into 1486 files. Approximately 25 percent of the data are observed values -- regional station data collections (separated primarily by contributors) and absolute gravity measurements. Grids and other derived summary data sets represent another 65 percent of the data. The remaining 10 percent of the disc contains geopolitical base map reference data and software.

This data set contains summary data for monthly leaf area index (LAI) and plant area index (PAI) at the km 83 Tower Site, in the Tapajos National Forest, Para, Brazil. LAI was estimated for hemispherical photographs of leaves collected between 2000 and 2003, using the histogram and gap-fraction analysis methods.There are two data files with this data set: one comma-delimited ASCII data file with this data set which contains the monthly summary LAI and PAI data, and one compressed (*.zip) file that contains hemispherical photo images (.bmp) for 2000-2001. The images include those taken pre-logging and post-logging at the measurement site for the purpose of comparing LAI. In addition, there is a companion file containing a program code developed for LAI analysis provided as an ASCII text file.

This Global Summaries dataset, known as GSOY for Yearly, contains a yearly resolution of meteorological elements from 1763 to present with updates applied weekly. The major parameters are: – average annual temperature, average annual minimum and maximum temperatures; total annual precipitation and snowfall; departure from normal of the mean temperature and total precipitation; heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; extreme annual minimum and maximum temperatures; number of days with fog; and number of days with thunderstorms. The primary input data source is the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. The Global Summaries datasets also include a monthly resolution of meteorological elements in the GSOM (for Monthly) dataset. See associated resources for more information. These datasets are not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which are not available in GSOM and GSOY. The GSOM and GSOY datasets replace the legacy U.S. COOP Summaries (DSI-3220), and have been expanded to include non-U.S. (global) stations. U.S. COOP Summaries (DSI-3220) only includes National Weather Service (NWS) COOP Published, or "Published in CD", sites.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains data from 1993 to the latest reporting year. These CSV format datasets are in normalized or ‘list’ format and are optimized for pivot table analyses. Here is a description of each file: - The RELEASES file contains all substance release quantities. - The DISPOSALS file contains all on-site and off-site disposal quantities, including tailings and waste rock (TWR). - The TRANSFERS file contains all quantities transferred for recycling or treatment prior to disposal. - The COMMENTS file contains all the comments provided by facilities about substances included in their report. - The GEO LOCATIONS file contains complete geographic information for all facilities that have reported to the NPRI. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information More NPRI datasets and mapping products are available here: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/access.html Supporting Projects: National Pollutant Release Inventory (NPRI)

4 computer laser optical discs ; 4 3/4 in. This DVD contains contains all State summary data files, documentation and software. System requirements: PC-compatible computer; DVD-ROM player; 8 GB of hard drive space for full installation (run from disc requires only about 9 MB); Internet browser.

analyze the national health interview survey (nhis) with r the national health interview survey (nhis) is a household survey about health status and utilization. each annual data set can be used to examine the disease burden and access to care that individuals and families are currently experiencing across the country. check out the wikipedia article (ohh hayy i wrote that) for more detail about its current and potential uses. if you're cooking up a health-related analysis that doesn't need medical expenditures or monthly health insurance coverage, look at nhis before the medical expenditure panel survey (it's sample is twice as big). the centers for disease control and prevention (cdc) has been keeping nhis real since 1957, and the scripts below automate the download, importation, and analysis of every file back to 1963. what happened in 1997, you ask? scientists cloned dolly the sheep, clinton started his second term, and the national health interview survey underwent its most recent major questionnaire re-design. here's how all the moving parts work: a person-level file (personsx) that merges onto other files using unique household (hhx), family (fmx), and person (fpx) identifiers. [note to data historians: prior to 2004, person number was (px) and unique within each household.] this file includes the complex sample survey variables needed to construct a taylor-series linearization design, and should be used if your analysis doesn't require variables from the sample adult or sample c hild files. this survey setup generalizes to the noninstitutional, non-active duty military population. a family-level file that merges onto other files using unique household (hhx) and family (fmx) identifiers. a household-level file that merges onto other files using the unique household (hhx) identifier. a sample adult file that includes questions asked of only one adult within each household (selected at random) - a subset of the main person-level file. hhx, fmx, and fpx identifiers will merge with each of the files above, but since not every adult gets asked thes e questions, this file contains its own set of weights: wtfa_sa instead of wtfa. you can merge on whatever other variables you need from the three files above, but if your analysis requires any variables from the sample adult questionnaire, you can't use records in the person-level file that aren't also in the sample adult file (a big sample size cut). this survey setup generalizes to the noninstitutional, non-active duty military adult population. a sample child file that includes questions asked of only one child within each household (if available, and also selected at random) - another subset of the main person-level file. same deal as the sample adult description, except use wtfa_sc instead of wtfa oh yeah and this one generalizes to the child population. five imputed income files. if you want income and/or poverty variables incorporated into any part of your analysis, you'll need these puppies. the replication example below uses these, but if that's impenetrable, post in the comments describing where you get stuck. some injury stuff and other miscellanea that varies by year. if anyone uses this, please share your experience. if you use anything more than the personsx file alone, you'll need to merge some tables together. make sure you understand the difference between setting the parameter all = TRUE versus all = FALSE -- not everyone in the personsx file has a record in the samadult and sam child files. this new github repository contains four scripts: 1963-2011 - download all microdata.R loop through every year and download every file hosted on the cdc's nhis ftp site import each file into r with SAScii save each file as an r d ata file (.rda) download all the documentation into the year-specific directory 2011 personsx - analyze.R load the r data file (.rda) created by the download script (above) set up a taylor-series linearization survey design outlined on page 6 of this survey document perform a smattering of analysis examples 2011 personsx plus samadult with multiple imputation - analyze.R load the personsx and samadult r data files (.rda) created by the download script (above) merge the personsx and samadult files, highlighting how to conduct analyses that need both create tandem survey designs for both personsx-only and merg ed personsx-samadult files perform just a touch of analysis examples load and loop through the five imputed income files, tack them onto the personsx-samadult file conduct a poverty recode or two analyze the multiply-imputed survey design object, just like mom used to analyze replicate cdc tecdoc - 2000 multiple imputation.R download and import the nhis 2000 personsx and imputed income files, using SAScii and this imputed income sas importation script (no longer hosted on the cdc's nhis ftp site). loop through each of the five imputed income files, merging each to the personsx file and performing the same set of...