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The current interactive data query page on the DRC features data and resources for the National Survey of Children’s Health (NSCH) including:

• Single year NSCH data from 2022
• Combined year NSCH data from 2021-2022
• Archived data is also available for
• Single years (2016, 2017, 2018, 2019, 2020, 2021)
• Combined years (2016-2017, 2017-2018, 2018-2019, 2019-2020, 2020-2021)
• 5-year (2016-2020) NSCH-derived National Outcome and Performance Measures

The Census Bureau, in partnership with MCHB, recreated weights for the NSCH survey and retroactively applied them to all surveys occurring before 2022. The newly adjusted weights better account for race and ethnicity data at the national and state levels. NSCH survey years 2016-2021 are now stored on the NSCH Data Archive page. Estimates from 2016-2021 on the DRC (which do not contain updated weights) should not be compared with estimates beginning with the 2022 single year data set (and the 2021-2022 combined year data set). Likewise, US maps and Interactive State Comparison Tables from NSCH years 2016-2021 are also archived on separate pages and should not be compared with current estimates based on the new weights.

Yes, revised estimates with the new weights can be created by applying the codes available in the DRC codebooks to the updated public use files provided on the U.S. Census Bureau’s website. Please read the weighting revisions technical document for more information.

• The full complement of 2022-2023 NSCH Child Family Child and Family Health Measures will be released to the interactive data query in December.
• Combined year 2022-2023 NSCH DRC constructed datasets and codebooks will be available January 2025.
• Single year 2023 NSCH data will be available Spring 2025.

A major revision to the NSCH took place in 2016. Among other changes, the National Survey of Children with Special Health Care Needs (NS-CSHCN) was integrated into the new NSCH. Pre-2016 NS-CSHCN and NSCH survey data and data resources including codebooks can still be found on the website and datasets are still available for request.

Please note, these earlier data should NOT be compared with the 2016 NSCH or any year thereafter.

Data and survey resources from the following surveys can be found on the CAHMI website here:

• The National Health Interview Survey - Child Complementary and Alternative Medicine (CAM) Supplement (2012)
• The National Health Interview Survey – selected variables from Child Component (2010-2014)
• The Survey of Pathways to Diagnosis and Services (2011)

Detailed information about the NSCH - including Fast Facts and FAQs, Survey Sampling and Administration Diagrams, Guides to Topics and Questions, Survey Methodology, Survey Instruments, Survey Content Maps, Guides to Survey Changes Across Years, and SPSS, SAS, and Stata Codebooks - are available on the DRC website.

The NSCH public use data files, along with FAQs, Methodology Reports, and other relevant background information and documentation, are located on the HRSA MCHB website and the U.S. Census Bureau website.

Point-and-click data is available through our Interactive Data Query, Comparison tables, and US Maps found on the homepage of our website, childhealthdata.org. In addition, DRC datasets and codebooks, available in 4 formats (STATA, SPSS, SAS, and CSV) are available for download on our Request NSCH Datasets page.

There are 3 ways to compare state data: using the Interactive Data Query, Comparison Tables, and US Maps. For instructions on how to compare data across states, please click here.

Visit our Request NSCH Datasets page, and complete a User Form and Data User Agreement. Once we have received a completed User Form and Data User Agreement, we will send you an e-mail with links to download all datasets and supplementary information.  

Please check your spam folders if you do not see an email containing all DRC datasets and codebooks. The email should be sent automatically from the info@cahmi.org email address. 

This mainly occurs due to SAS operating system compatibly issue. The DRC SAS datasets are created in SAS 64-bit operating system. If you are using SAS 32-bit version, you will need to create a SAS format file in your operating system using the “Create formats_xxxxNSCH_DRC” SAS program file provided in the compressed folder with the dataset.

Information on how to combine the datasets and adjust the weights is available in the NSCH Guide to Multi-Year Analysis provided by the U.S. Census Bureau.

DRC constructed datasets provide state and national level data. The NSCH is designed to be representative at the national and state level only; however, it is possible to analyze county and zip code data on-site at the U.S. Census Bureau’s Research Data Centers. Interested researchers must first complete the process to receive Special Sworn Status to be able to gain access to the datafiles on-site. Go to https://ask.census.gov/support/case to submit your request to begin this process.

The NSCH reports four geographic variables for some states in the public use file: FIPSST (State of Residence), CBSAFP_YN (Core-Based Statistical Area Status), METRO_YN (Metropolitan Statistical Area Status), and MPC_YN (Metropolitan Principal City Status). More information about the geographic variables can be found in the U.S. Census Bureau’s Methodology Report.

Synthetic estimates are also a way in which you can obtain local estimates using national or state-level data. A synthetic estimate is a prevalence estimate for a local area that is calculated by applying state-level NSCH prevalence estimates by demographic characteristics (e.g., poverty level and/or race/ethnicity) to the demographic distribution for local areas obtained from an external source (e.g., American Community Survey). It is similar in concept to an indirect adjustment. A simple approach to this indirect adjustment can only accommodate two variables at one time and thus will not produce robust estimates found in multilevel approaches. For more information on creating straightforward synthetic estimates, view our Local Uses of National and State Data Brief. Although this approach has limitations, it offers an accessible alternative to direct estimation.