NIS data are available from through , which allows analysis of trends over time. The NIS is sampled from the State Inpatient Databases SID , including all inpatient data that are currently contributed to HCUP. The number of States participating in the NIS has grown from 8 in the first year to 47, plus the District of Columbia, at present.

Beginning with the data year, the NIS approximates a percent stratified sample of all discharges from U. community hospitals, excluding rehabilitation and long-term acute care hospitals.

The NIS was redesigned to improve national estimates. To highlight the design change, beginning with data, AHRQ renamed the NIS from the " Nationwide Inpatient Sample" to the " National Inpatient Sample.

The new sampling strategy produces more precise estimates than the previous NIS design by reducing sampling error. For many estimates, confidence intervals under the new design are about half the length of confidence intervals under the previous design.

Changes to the NIS may impact some types of analyses. For example, the elimination of hospital identifiers means that hospital linkages can no longer be done with the NIS and the sampling of discharges means that analyses relying on a census of discharges from sampled hospitals e.

hospital volume analysis can no longer be performed. Because inpatient data are available for many individual States through the HCUP Central Distributor, state inpatient data can be used for those analyses that require a census of discharges from individual hospitals, local market areas, and States.

For a detailed description of the NIS redesign, please see the NIS Redesign Report. For more details on the NIS, see the Introduction to the NIS, PDF file, 1. The NIS contains clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals as required by data sources.

It contains clinical and nonclinical data elements for each hospital stay, including:. As a uniform, multi-State database, the NIS promotes comparative studies of healthcare services and supports healthcare policy research on a variety of topics, including:. Spanning more than 20 years of data, the NIS is ideal for longitudinal analyses.

However, the database has undergone changes over time, including the sampling and weighting strategy used. Users of the NIS should expect a one-time decrease to historical trends for discharge counts of about 4 percent beginning with data year Users should also expect smaller one-time disruptions to historical trends for rates and means estimated from the NIS, beginning with data year For trends analysis using NIS data and earlier, revised weights should be used to make estimates comparable to the new design beginning with data.

These new discharge trend weights replace the earlier NIS Trend Weights that were developed for the NIS following the NIS redesign. The new trend weights are available for download as ASCII files, along with SAS®, SPSS®, and Stata® load programs, under NIS Trend Weights Files from the NIS Database Documentation page on the HCUP-US website.

The report Using the HCUP National Inpatient Sample to Estimate Trends , available on the HCUP-US website under Methods Series , includes recommendations for trends analysis. NIS releases for data years through are available for purchase online through the Online HCUP Central Distributor.

All HCUP data users, including data purchasers and collaborators, must complete the online HCUP Data Use Agreement Training Tool , and must read and sign the Data Use Agreement for Nationwide Databases PDF file, KB; HTML. Email: HCUP AHRQ. gov Telephone: toll free.

The NIS Database is distributed as fixed-width ASCII-formatted data files delivered via secure digital download from the Online HCUP Central Distributor.

The files are compressed and encrypted with 7-Zip ©. To load and analyze the NIS data on a computer, users will need the following:. The data set includes weights for producing national and regional estimates. NIS documentation and tools, including programs for loading the ASCII file into SAS, SPSS, or Stata beginning with , are also available on the NIS Database Documentation page.

Home About Need Help? Do Your own analysis. HCUP Supplemental Files NIS-Trend Weights Files NIS Hospital Ownership Files NIS Discharge-Level Supplemental Files KID-Trend File Cost-to-Charge Ratio CCR Files Hospital Market Structure HMS Files Supplemental Variables for Revisit Analyses American Hospital Association AHA Linkage Files AHRQ Quality Indicators QIs.

REQUEST DATA. DATA QUERY TOOLS. NIS Overview Overview of the National Nationwide Inpatient Sample NIS The National Nationwide Inpatient Sample NIS is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project HCUP.

For more details, see NIS Database Documentation and the Introduction to the NIS, PDF file, 1. Contents: What's New in the NIS? About the NIS NIS Data Elements NIS File Structure NIS Areas of Research and HCUP Publications The NIS and Multi-Year or Trend Analyses Purchase the NIS NIS Hardware and Software Requirements.

The overall number of discharges for data year increased by 3 percent from In the first quarter of , the number of discharges was lower by 7 percent compared to the first quarter of However, discharges in increased 16 percent in the second quarter, 3 percent in the third quarter, and less than 1 percent in the fourth quarter.

The overall in-hospital mortality rate increased from 2. Revisions to how hospitals are defined - the NIS now uses the definitions of hospitals and discharges supplied by the statewide data organizations that contribute to HCUP, rather than the definitions used by the AHA Annual Survey.

Revisions to enhance confidentiality - the NIS now eliminates State and hospital identifiers and other data elements that are not uniformly available across States. Key features of the most recent NIS include: The NIS is drawn from all States participating in HCUP, covering more than 97 percent of the U.

The NIS approximates a percent stratified sample of discharges from U. The self-weighting design of the new NIS reduces the margin of error for estimates and delivers more stable and precise estimates than previous versions of the NIS. Importantly, this document is prepared prior to any sample collection.

Ultimately, the QAPP is a project feature that decision makers will use to assess the overall quality of the generated information. Another term you may see is quality control "QC" , which refers to the overall system of technical activities that are designed to measure the quality of information.

Although the EPA lists twenty-four distinct issues that can be addressed in a QAPP, we focus here on various themes that we deem especially important and useful in the context of citizen science projects: i management description, ii sampling design, iii sample collection methodology, iv sample handling and custody, v sample analysis, vi quality controls, and vii data interpretation.

The EPA has issued a vast number of very specific and detailed protocols for the measurement of pollutants in various contexts i. Including this level of detail here is impractical. Instead, we offer broad, generalizable suggestions and provide additional resources for those who seek greater detail for their individual project needs.

While some projects are small enough that a single person can successfully complete them, many will require the coordinated efforts of many individuals.

When projects involve groups of individuals, establishing and describing management roles at the onset of the project is important for ensuring project consistency and cohesiveness. Project managers must among many other responsibilities : i identify funding resources and control expenditures of funds; ii establish what, when, how, and by whom samples will be collected, analyzed, and interpreted; iii ensure that volunteers understand how to clean and calibrate instrumentation; and iv assure, if needed, the proper training of those involved in the project e.

Project managers should also seek to maximize the use of community expertise. For example, even if you lack the training or expertise to design or complete a project, your community may include individuals with technical or scientific training who are willing and eager to participate e.

Sampling design includes considering the types of samples that will be collected and when and where they will be collected. Sampling design decisions implicate multiple factors that impact information quality, but it is primarily concerned with the representativeness of the information.

A well-developed sampling design plays a central role in ensuring that conclusions are adequately supported by data. Thinking about your sampling design at the beginning of a project can help avoid introducing bias at the onset of information generation.

In some aspects, your sampling design will be dependent on the type of sample you are collecting. For example, the placement of air monitors depends on the sampling objective: ground level monitoring, air mass i. Likewise, water and soil sampling designs can include details concerning the location and depth at which samples will be collected.

The sampling design should include documentation of when and where samples will be collected, including, for example, the following types of information:. Addressing these issues will help reduce potential bias in the ultimate conclusions and promote the quality of the information generated in a project.

Selecting sampling typically involves one of two approaches: i random or probabilistic sampling and ii judgmental sampling. While each approach has advantages and disadvantages that can be discussed at length, this discussion merely serves to introduce the topics.

In random sampling, as its name implies, sampling locations are chosen randomly. It is most useful when the pollutant of interest is relatively homogeneous in the sampling medium i. Because citizen science projects concerned with environmental problems often focus on a pollutant source , random sampling may be less commonly used relative to judgmental sampling.

Judgmental sampling, as its name implies, involves the selection of sampling locations based on judgment. Judgmental sampling is most useful when there is historical or physical knowledge of the feature or condition under investigation: for example, when the impact of the pollutant can be visually discerned or when the location of pollutant release is known.

Ultimately, the sampling design should match the needs of the project with the resources available e. A well-designed sample collection methodology helps ensure the precision and accuracy of the information that is ultimately generated. The primary question addressed by a sample collection methodology is: how will samples be collected during each sampling event e.

The answer to this question may include, among other things, a description of: i the number of samples to be collected during each sampling event i. Some aspects of sample collection methodologies are highly generalizable across projects.

For example: [8]. For example, air sample collection methodologies are generally highly specific to the instrumentation used. Water samples should be collected with as little agitation to the water as possible. Wading or streamside sampling increases the probability of agitation. In instances when agitation is a concern, samples should be collected while facing upstream.

Moreover, water sample containers should be filled to their capacity i. Unpreserved and preserved samples have holding times of one week and two weeks, respectively. Holding times indicate the period during which the samples should be tested. The alternate shoveling method involves placing a spoonful of soil in each container in sequence and repeating until the containers are full or the sample volume has been exhausted.

Sample collection methodologies may also contemplate other ways of documenting sample collection. For example, a methodology could direct volunteers to photograph, videotape, or otherwise record the actual sample collection to demonstrate that the activity complies with the sample collection methodology.

Typically, notes of visual and olfactory observations should be recorded in a log book to describe, for example, the depth of each sample, whether its color and texture, any odors, etc.

The log can also be used for demonstrating sample handling and custody and any field analyses of the samples. Precision and accuracy are the main information quality concerns addressed by the establishment of sample handling procedures.

These procedures apply to projects that do not perform sample analysis in the field. In these instances, the samples must be transported to an alternative site , such as a laboratory. Chain-of-custody procedures should be established to keep track of all samples that will be shipped or transported to a laboratory for analysis i.

This information is important for authentication of any information generated by analysis of the samples discussed in Chapter 2.

Analysis of samples may occur in the field or in a laboratory. In either case, the analytical methods and equipment used in the analysis should be documented. In addition, documentation of instrumental calibration, inspection and maintenance should be provided.

These procedures promote precision and accuracy of the data. Generally, analytical tools that are EPA approved are documented in the Federal Register. In some instances, the EPA provides lists of analytical tools that are EPA-approved when used in specific contexts.

The design of a project should include methods for collecting and testing quality control samples; examples include field controls, equipment controls, split samples, replica samples, and spiked samples.

Because the abundance of the analyte i. pollutant is known in each of these control samples, they are useful in assessing the precision and accuracy of the data that is ultimately generated. The project design should include considerations of how the data generated from sample analysis will be interpreted.

It is from this interpretation that conclusions will be drawn. In some instances, you, the citizen scientist, may be able to interpret the data. However, as mentioned in Chapter 2 , some uses of information generated from your project will require expert interpretation.

When data is interpreted by a qualified expert, the quality of the information is enhanced. There are likely to be qualified experts in your community who are willing to assist you. Think about universities, community colleges, high schools, and locally-based environmental engineering companies.

Previous Chapter Next Chapter. Protection Agency, CIO pdf ; see also Frequently Asked Questions: Quality Assurance Project Plans , U.

last visited June 1, ; Quality Assurance Handbook and Guidance Documents for Citizen Science Projects , U. last visited June 1, Protection Agency, Quality Assurance Template for Citizen Science Projects Apr.

pdf ; U. Protection Agency Region 4, SESD Operating Procedure: Soil Sampling, SESDPROCR3 Aug. Protection Agency Region 4, SESD Operating Procedure: Surface Water Sampling, SESDPROCE3 Feb.

Protection Agency Region 4, SESD Operating Procedure; Groundwater Sampling, SESDPROCR3 Mar. Protection Agency, List of Designated Reference and Equivalent Methods Dec.

at View the discussion thread. The Citizen Science Manual A Guide for Starting or Participating in Data Collection and Environmental Monitoring Projects. Introduction 1. Identifying your Project's Focus and Designing its Approach 2.

Identifying Project Goal 3. Public Information 4. Potential Liability 5. Information Generation - Design of Sample Collection, Sample Analysis, and Data Interpretation Methodologies Assessing Information Quality Quality Assurance Protection Plan Guidelines 6.

Information Use Resources Supplement 1: Public Rights to Information About Chemical Storage and Releases Supplement 2: Using Citizen Science Data in Litigation 50 State Map Glossary Questions and Answers. Search form Search.

Sample data for testing your applications This database was prepared using a combination of materials that include aerial photographs, topographic maps (, and ,), field notes Missing

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