Oversampling in the NSCH is an option to enable reporting for specific population groups, conditions, or localities. Oversampling increases the households sampled in order to increase the number of completed surveys.
When there are more data, patterns within specific populations can be examined [e.g., looking at specific racial and ethnic groups within the more general population of children and youth with special health care needs (CYSHCN)]. By allowing more focused assessment, oversampling extends the utility of the survey to meet state and local needs, helping them to plan and direct investments for greatest impact.
Types of oversampling available
We offer two types of oversampling:
- General
- Increases the overall sample size to support subgroup analyses (e.g., children and youth with special health care needs) or more precise annual estimates
- Targeted
- Increases sample size for certain geographic areas to produce city, county, or regional estimates
- Increases sample size, and more precise estimates, for racial and ethnic groups who are concentrated in certain geographic areas
Cost involved
The cost of oversampling depends on the type, size, and number of years to achieve sample size objectives. Since 2020, actual annual costs have ranged from $20,000 to $500,000 per oversample.
Federal Title V MCH Block Grant allocations can fund oversamples prior to state disbursement. The state Title V Director must make a request through their Project Officer to use this mechanism. Other state and private funds can also be used by establishing a contractual agreement directly with the U.S. Census Bureau.
Timeline
A state must finalize their plans by July before the data collection year (e.g., July of 2024 for a 2025 oversample). This allows time for routing and signatures.
The U.S. Census Bureau releases NSCH data in the October following the oversample year (e.g., 2022 data were released in October 2023).
Accessing data
Our grantee, The Center for Adolescent Health and Measurement Initiative, provides an interactive web query system to view pre-calculated data tables, charts, and maps for a variety of indicators and characteristics. Public use files are also available for download for those wishing to conduct their own analysis; however, the U.S. Census Bureau requires an approved project proposal from a Research Data Center to access sub-state geographic identifiers. Research Data Center access may take up to a year to establish and may involve additional fees. To protect confidentiality, any sub-state estimates produced from restricted access files must be approved by the Census Bureau’s Disclosure Review Board prior to public release.
Current state participation
Since 2020, 17 states and one metro area have sponsored oversamples that have included a range of both single and multi-year efforts. In alignment with the our Maternal and Child Health Bureau strategic plan (PDF - 590 KB) focus on equity, the most common objective included in ten oversample plans is to improve estimates for specific racial and ethnic groups.
State oversampling types, objectives, and years
State |
Type |
Objective |
2020 |
2021 |
2022 |
2023 |
2024 |
California |
Local Area + Race/Ethnicity |
To enable county estimates (150+ interviews in each of 41 counties) and regional reporting by race/ethnicity and CYSHCN using 3-year data. Learn more about California’s current effort to use oversampling |
|
|
X |
X |
X |
Colorado |
Local Area |
To enable regional estimates (150+ interviews per year in each of eight regions) |
X |
X |
X |
X |
X |
Georgia (Atlanta) |
Local Area |
To achieve 1,000 total interviews in the Atlanta metro area for reliable anxiety and depression estimates by age |
|
X |
X |
X |
X |
Illinois |
General + Race/Ethnicity |
To achieve 2,000+ total interviews and 300 Black interviews for subgroup analyses using 2-year data |
|
|
|
X |
X |
Kansas |
General |
To achieve 4,000 total interviews and ~1,000 CYSHCN for subgroup analyses using 2-year data |
|
|
|
X |
X |
Louisiana |
General + Race/Ethnicity |
To achieve ~1,500 total interviews for fully reliable Title V performance measures and 300 Hispanic interviews for reportable Title V performance measures using 2-year data |
|
X |
|
X |
X |
Minnesota |
General + Race/Ethnicity |
To achieve 500 CYSHCN interviews for subgroup analyses and boost Black and American Indian/Alaska Native interviews for reportable estimates |
|
|
|
X |
X |
Nebraska |
General |
To achieve ~1,500 total interviews for fully reliable Title V performance measures using 2-year data |
X |
X |
X |
X |
X |
New Mexico |
Race/Ethnicity |
To increase American Indian/Alaska Native interviews (150+) for reliable overall estimates using 2-year data |
|
|
|
X |
|
New York |
Local Area + Race/Ethnicity |
To enable regional estimates for 12 counties or county groups (~300 interviews each) and subgroup analyses for Black and Hispanic populations (~2,000 extra interviews) |
|
|
X |
|
|
Ohio |
Race/Ethnicity |
To achieve 200+ Black and Hispanic interviews for reliable estimates of CYSHCN and other key indicators using 2-year data |
|
X |
X |
X |
X |
Oregon |
Race/Ethnicity |
To enable reporting for all racial/ethnic groups (30+ interviews for each group) using 2-year data |
X |
X |
X |
|
|
Pennsylvania |
General |
To improve overall precision through a general oversample achieving 1,500+ interviews per year |
|
|
X |
X |
X |
South Carolina |
General |
To improve overall precision and enable subgroup analyses through a general oversample achieving ~800 additional interviews |
|
|
|
|
X |
Utah |
Race/Ethnicity |
To enable reporting for all racial/ethnic groups (30+ interviews for each group) using 2-year data |
|
|
|
|
X |
Tennessee |
Race/Ethnicity |
To achieve 300+ Black and 250+ Hispanic interviews for subgroup analyses using 2-year data |
|
|
X |
X |
|
Wisconsin |
General |
To enable CYSHCN subgroup analyses (500+ interviews) using 2-year data |
X |
X |
|
X |
X |
Wyoming |
General |
To improve overall precision, particularly for CYSHCN, through an oversample achieving 600 additional interviews |
|
|
X |
X |
X |
Example Results and Outcomes
States who oversampled significantly increased their sample size and achieved their objectives.
Information to Help Plan and Design an Oversample
State Oversampling in the National Survey of Children’s Health: Feasibility, Cost, and FAQs (PDF - 303 KB)
Contact Us
Ashley Hirai, PhD, Senior Scientist
Office of Epidemiology and Research