NOMS Method

Where NOMS Data Come from and How the Data are Analyzed

NOMS Data Sources

Data for NOMS come from death certificates issued by state vital records offices.

Population

The NOMS data include all men and women employed in a specified occupation or industry (work done during most of working life, reported by funeral directors on death certificate), ages 18-90, who died at any time during the specified years of the analysis (1985-1998 or 1999, 2003-2004, 2007–2014).

Because data were not available for the entire population of men and women at risk of death in the occupations and industries reported on the death certificates, we evaluated proportionate mortality based on cumulative deaths over the time period studied.

How NOMS data are analyzed: The PMR Query System

The PMR Query System provides age-adjusted underlying cause of death proportionate mortality ratios (PMRs) for the total population and for race/sex combinations for each of the data sets. You can examine cause of death by industry or by occupation using the PMR Query System.

Why we calculated PMRs

PMRs are usually computed when data for the population at risk are not available and rates of death or standardized mortality ratios (SMR) cannot be calculated.

Learn more about PMRs

How the PMR Query System works

The 2011 Proportionate Mortality Ratio Analysis System (PMRAS) was created to calculate the PMRs for the PMR Query System.

• PMRAS calculates PMRs by comparing the proportion of deaths from a specified cause within a specified occupation or industry group with the proportion of deaths due to that cause among all decedents and age-adjusts after stratifying on race (white, Black).
• Unemployed, students, volunteers, and those in unknown occupations or industries (less than three percent), were excluded from the analysis.
• PMR statistics are suppressed for any occupations or industries with fewer than 5 deaths.

Cause of death, industry and occupation lists

• The Tenth Revision International Classification of Diseases (ICD) codes were used to code deaths that occurred January 1, 1999 forward
• The Ninth Revision ICD codes were used to code deaths that occurred during 1985-1998
• 1990 or 2000 US Census codes were used to code a decedent’s usual industry or occupation (Industry and Occupation Categories)

Strengths

• Full coverage of states that participate, all death records with industry and occupation data are included.
• Large number of records allow analysis of more specific occupation and industry groups, as well as demographic groups.
• NOMS also has broad geographic coverage, which will be improved in the future.
• Provides baseline (surveillance) data on chronic disease mortality for NIOSH’s industry sector programs.
• Allows NIOSH to monitor trends and generate hypotheses regarding associations between occupational risk factors and specific health outcomes, particularly in chronic disease mortality.
• Data can also be used to inform policy and direct intervention or prevention efforts to specific industries and occupations and demographic groups with the greatest burden of disease.
• Will enable NIOSH to estimate chronic disease burden for all industry sectors and several health cross-sectors.

Limitations

• Limitations in the PMR method may bias risk estimates toward the null.
• Only information on the usual or longest held occupation and industry are available, not length of employment, or employment that was of shorter duration.
• Misclassification may be a source of bias due to inaccurate reporting of usual occupation and industry or cause of death, and lack of occupational exposure information. While the degree of misclassification of cause of death varies by disease, fatal chronic disease such as lung cancer is more accurately classified than many other causes of death^[1].
• 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 may be influenced by confounding factors.
• A lack of significantly increased PMRs may represent the selection of healthy workers for particular occupations or industries. However, recent studies suggest that PMR analysis used for population-based studies may be less biased than cohort study analysis because comparison with other workers lessens the influence of the healthy worker effect.

[1] Kircher T, Nelson J, Burdo H. 1985. The autopsy as a measure of accuracy of the death certificate. N Engl J Med 313:1263-1269.