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NATIONAL OCCUPATIONAL MORTALITY SURVEILLANCE (NOMS)

Frequently Asked Questions

What is NIOSH?


Information Related to Surveillance

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What is public health surveillance?

Public health surveillance is the ongoing, systematic collection, analysis, interpretation and dissemination of health-related data for public health action.

What is occupational mortality surveillance and why is it important?

Occupational mortality surveillance uses data to identify trends in the work-related deaths.  By looking at trends, we can identify jobs and work environments that may be more hazardous to work in. The information we gather from assessing these trends, enables researchers and health professionals to further assess causes for workplace illnesses, injuries and deaths.

About NOMS

What is NOMS?

NOMS stands for National Occupational Mortality Surveillance. It consists of databases that contain more than 15 million death records collected from 30 states during the periods 1985–1998 and 1999, 2003–2004, 2007–2013. Data from 2014 are expected to be added to the database in 2019.

What is the source of NOMS data?

NOMS data are from death certificates issued by state vital statistics offices.

Who fills out the death certificates?

Death certificates are completed by a funeral director and medical certifier. The funeral director completes the demographic information including the usual occupation and industry (kind of business). The medical certifier completes the cause and circumstances of the death.

Who codes the death certificate cause of death information and what classification system is used to provide a standardized code?

The National Center for Health Statistics codes cause of death narratives entered into death certificates— by the medical certifier or person who pronounces the death—according the International Classification of Diseases (Mortality) ICD10.

What death certificate information does NOMS include?

Death certificates document many aspects of a person’s life, including where and how they died. NOMS uses a limited number of fields such as:

  • cause of death
  • usual occupation and kind of business or industry
  • demographic data, such as age, sex, race, and ethnicity

Is there a Bibliography of published articles that used NOMs data?

Yes. See our Publications page.

Can NOMS data be released to external requestors?

NOMS data may not be released due to data agreements with the US states and the National Center for Health Statistics (NCHS), though NOMS data analysis can be done using the Proportionate Mortality Ratio (PMR) Query System.

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What occupation and industry codes are used?

Standard death certificates include fields for industry and occupation narratives. NIOSH developed a computer program to use these narratives to assign standard codes developed based on US Census Bureau codes. Data are coded to both the Census 1990 and Census 2000 Industry and Occupation codes depending on when they were coded and whether they were coded by states or by NIOSH. See the NIOSH Industry and Occupation Coding Topic Page to learn more.

What is cause of death?

The cause of death on death certificates is completed by the person who pronounces or certifies the death. They determine the chain of events—diseases, injuries, or complications—that directly led to the death, as well as other significant conditions contributing to death.

What is underlying cause-of-death?

The underlying cause of death is the disease, injury, or circumstance that initiated the chain of morbid events that led directly and inevitably to death.

What is the International Classification of Disease (ICD)?

International Classification of Disease is the standardized coding system that categorizes disease and is used by nearly all countries. It was developed by the World Health Organization and is adapted for use in the United States.

Information about Proportionate Mortality Ratios (PMRs)

What is a PMR?

PMR stands for proportionate mortality ratio, and is computed when the total population at risk of death 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.

what is PMR

PMRs may be computed for industry groups as well. PMRs are computed when the population at risk of death for a specific occupation is not known.

How do we interpret a PMR result?

PMRs are not meant to be stand-alone estimates of death rates, and must be interpreted in context with other relevant scientific data. These could include occupational exposures, biologic plausibility, and the results of other studies.

Example from the NOMS PMR Query web site:

Example from the NOMS PMR Query web site

For NOMS data for the years 1999, 2003-2004, 2007–2010, 58 Painters, Paperhangers, & Plasters (Census 2000 Occupation Codes 642–643, 646; Census 1990 Occupation Codes 556, 579–584; Census 1980 Occupation codes 556, 579–584) died from Malignancy of the Larynx (ICD-10 code C32, ICD-9 Code 161). The PMR is 209.  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. Therefore, Painters, Paperhangers, & Plasters, are more than twice as likely to die from Malignancy of the Larynx as U.S. workers in general.

Confidence intervals that do not include 100 indicate that the PMR is statistically significant. In this case, the confidence interval is 159–270, which does not include 100, so this PMR is statistically significant.

Epidemiologists and other researchers use the findings reported from NOMS data as an initial step in deciding to investigate them more fully by designing an epidemiologic study.

NOTE: Some researchers do not multiply their PMRs by 100. A PMR of 209 and 2.09 can be equivalent.

What is a death rate?

A death rate is computed by dividing the tabulated deaths by the number of people at risk of dying over a defined time interval.

What are some of the drawbacks to using PMRs?

Since PMRs are based on proportions, which by definition add to one; when one proportion is high others are therefore low. For example, loggers have a very high fatal injury mortality rate, deceptively low PMRs for other causes can result.

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