National Occupational Mortality Surveillance

The National Institute for Occupational Safety and Health (NIOSH) maintains the NOMS (National Occupational Mortality Surveillance) System database of death certificate data with coded occupation and industry information. NIOSH and other investigators use these data to survey association of cause-specific mortality and occupation and/or industry. Twenty-eight states have participated in the project for two or more years from 1984 through 1998.^* Although the NOMS database lacks information on length of employment, specificity of job description, or estimates of workplace exposures, its advantages over recent studies include its size and its broad geographic coverage, and the recent date of death of the cases. Depending on date of death, usual occupation and industry of the decedent are coded according to the 1980 and 1990 Bureau of the Census classification system¹. Cause of death is coded according to the 9th Revision of the International Classification of Diseases ².

The measure of association used most often is the proportionate mortality ratio (PMR)³. The PMR indicates only whether the age- standardized proportion of deaths from a specific cause of death for a particular occupation or industry appears to be higher or lower than the expected proportion for a particular occupation or industry. Age stratification is done by five-year age groups for ages 15-90 years. To test for statistical significance of the PMR, two-sided 95% confidence intervals (95% CI) are calculated, based on the Poisson distribution for observed deaths⁴, and using the normal approximation to the Poisson for large numbers⁵.

A statistically significantly elevated PMR cannot be interpreted directly as indicating a causal relationship between the industry or occupation and the cause of death. When a very large number of PMRs are tested for statistical significance, many of the elevated or decreased PMRs will occur due to chance. Other elevated PMRs will be due to confounding factors. A lack of significantly increased PMRs may represent the selection of healthy workers for particular occupations or industries.

Data from the NOMS system is available on the Web in the form of a query system for access to precalculated PMRs by occupation or industry. An overview of mortality in states by occupation and industry and state tables of PMRs for selected causes of death occupation and industry are also available at the same web site.

Researchers may also request statistics for particular causes of death, industries, occupations, or industry-occupation combinations. E.g., for a cause of death, NIOSH can produce PMRs for industries or occupations relative to that cause; for an industry or occupation, NIOSH can produce PMRs for causes of death. Analyses are usually for black and/or white men and/or women for young and old age groups, usually ages 18-64 and 65-120 years.

To request statistics or for more information, please contact James Walker at NIOSH, (513) 841-4219, or e-mail jtw1@cdc.gov.

* Alaska, Colorado, Georgia, Hawaii, Idaho, Indiana, Kansas, Kentucky, Maine, Missouri, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York except New York City, North Carolina, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, Tennessee, Utah, Vermont, Washington, West Virginia, and Wisconsin.

References

1. United States Bureau of the Census. 1980 Census of Population: Alphabetical Index of Industries and Occupations. Washington, DC: U.S. Government Printing Office, 1980; United States Bureau of the Census. 1990 Census of Population and Housing: Alphabetical Index of Industries and Occupations. Washington, DC: U.S. Government Printing Office, 1992.
2. World Health Organization. International Classification of Diseases. Manual of the International Statistical Classification of Diseases, Injuries, and Causes of Death. Based on the Recommendations of the Ninth Revision Conference, 1975. Geneva: World Health Organization, 1977.
3. Kupper LL, McMichael AJ, Symons MJ, Most BM. On the utility of proportional mortality analysis. J Chron Dis 31:15-22, 1978.
4. Bailar JC III, Ederer F. Significance factors for the ratio of a Poisson variable to its expectation. Biometrics 20:639-643, 1964.
5. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22:719-748, 1959.