Supplier
CDC/NCIPC and CPSC
Years Available
1993 to present
Periodicity
Annual
Mode of Collection
Surveillance data (passive data collection) collected at a sample of U.S. hospital emergency departments.
Description
The National Electronic Injury Surveillance System – Firearm Injury Surveillance Study (NEISS-FISS) collects data using the Consumer Product Safety Commission's (CPSC) National Electronic Injury Surveillance System (NEISS). NEISS-FISS monitors incidents of nonfatal firearm-related injuries treated in U.S. hospital emergency departments. NEISS collects injury data from a nationally representative sample of U.S. hospital emergency departments; NEISS-FISS is implemented in all hospitals that participate in NEISS and collects additional circumstance information specific to firearm-related injuries.
Selected Content
NEISS collects demographic data, cause and mechanism of injury, locale where injury occurred and product involved.
Population Covered
U.S. population
Methodology
NEISS hospitals are a stratified probability sample of all U.S. hospitals that have at least 6 beds and provide 24-hour emergency department (ED) services. Five strata are included, four representing hospitals of different sizes, and the fifth representing children's hospitals. At each NEISS hospital, ED staff enter information about the injury into the patient's electronic medical record. Patient records are then reviewed for data related to NEISS criteria. The injury information is transcribed in coded form and electronically sent to the NEISS program at CPSC where quality assurance coders review and complete the coding. Data are weighted to produce national estimates. Follow-back investigations to obtain more information about the likely causes of the incident are conducted for certain incidents through telephone and on-site interviews with the patient or patient's relative.
Response Rates and Sample Size
NEISS collected data from a sample of approximately 100 hospitals with 24 hour emergency department services.
Interpretation Issues
Due to a high percentage of unknowns in some variables like Race/Ethnicity, estimates need to be suppressed.
