National Occupational Mortality Surveillance (NOMS)
NOMS is a collaboration between NIOSH, the National Center for Health Statistics (NCHS), and jurisdictions. Working together, we assemble and code industry and occupation as part of death certificate data processing. The data then become part of the National Vital Statistics System (NVSS). Starting with 2020, data are available for public use. Older data are not available publicly.
NOMS was developed in the 1980s to monitor changes in the cause of death by usual occupation or industry in the United States. The initiative has evolved over time. Including industry and occupation data as part of the NVSS is a step forward for occupational research.
Having industry and occupation in the NVSS enables researchers to:
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- examine changes in cause of death by usual occupation and industry,
- identify new work-related health hazards, and
- guide research and prevention activities.
How Industry and Occupation Death Certificate Data are Collected
Each jurisdiction has its own electronic death registration system (EDRS) with information collected based on the 2003 US Standard Certificate of Death. Jurisdictions use this information to issue death certificates. Data for death certificates can be divided into two parts:
- Cause of death
Cause of death is filled out by the medical examiner, coroner, or another type of medical certifier. - Demographic information
Demographic information is filled out by the funeral director with help from a decedent’s informant. It includes narrative fields which describe the decedent’s usual industry and occupation. Learn how funeral directors can collect the best information.
How Industry and Occupation are Coded
Each jurisdiction collects and transmits the death certificate information to NCHS. NCHS pulls industry and occupation narratives and sends them to NIOSH. NIOSH then translates text to codes with expert coders and the NIOSH Industry and Occupation Computerized Coding System (NIOCCS).
Once all industry and occupation information has been assigned a code, NIOSH returns the coded data to NCHS. The industry and occupation codes are then sent back to the jurisdictions and added to the NVSS.
Jurisdiction Participation in Industry and Occupation Coding Program
| 2020 | 47 Vital Statistics Jurisdictions
Not included: Iowa, Arizona, North Carolina, Rhode Island, and Washington, D.C. |
| 2021 | 50 Vital Statistics Jurisdictions
Not included: Rhode Island, Washington, D.C. |
| 2022 forward | 52 Vital Statistics Jurisdictions, which includes 50 states, New York City, and Washington, D.C. |
Related Resources
Find NOMS-related publications produced by NIOSH authors and grantees by searching in NIOSHTIC-2. NOMS publications go back to the 1980s and include data sets that are no longer available.
Suggested Citation
NIOSH (2023). National Occupational Mortality Surveillance (NOMS). U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Division of Field Studies and Engineering, Health Informatics Branch. <website address> Date accessed._mm/dd/yyyy.
Frequently Asked Questions
PMR stands for proportionate mortality ratio. PMRs are a common way of analyzing death certificate data. PMRs are computed when the population at risk of death for a specific occupation is not known.
To calculate a PMR, the proportion of deaths due to a specific cause for a group of workers is divided by the proportion of deaths due to that cause in all workers.
PMRs can be computed for occupation or industry. Learn more and see an example of how PMRs can be used.
A PMR greater than 100 indicates that a particular cause accounts for a greater proportion of deaths in the population of interest than is expected based on the proportion of deaths due to this cause in all workers.
Confidence intervals that do not include 100 indicate that the PMR is unexpectedly high or low.
Considerations when using PMRs
- PMRs can be computed when the underlying population at risk cannot be determined (lack of denominator data for calculating rates). For example, data can be analyzed by demographic subgroups including racial/ethnic groups to monitor for inequities.
- A PMR for one cause of death is dependent on other causes of death, if one cause of death is elevated other causes of death will be depressed.
- PMRs may bias risk estimates toward the null.
- An elevated PMR does not indicate a direct causal relationship between the industry or occupation and the cause of death.