NATIONAL OCCUPATIONAL MORTALITY SURVEILLANCE (NOMS)

Methods

The NOMS database includes data for 9,964,280 US workers who resided and died during the period 1985-1998 in one of 30 U.S. states (see maps at about NOMs page). An additional 3,731,310 deaths were recently added that occurred in 25 U.S. states 1999, 2003–2004, 2007–2010, totaling 13,695,590 deaths. When characterized by race and sex, the 3,731,310 deaths during 1999, 2003–2004, 2007–2010 included 1,602,754 white males, 186,631 Black males, 1,664,753 white females, 179,413 Black females, 51,692Asian/American Indian/Pacific Islander (AIP) males, and 46,067 AIP females. When stratified by ethnicity, there were 111,846 deaths in Hispanic males and 89,759 in Hispanic females.

PMR Charts
For the PMR Charts, multiple cause proportionate mortality were calculated and displayed with all race and genders combined for two time periods: 1999, 2003-2004, 2007-2010 and 1985-1998. Over 3000 charts display PMRs for cancer and chronic disease stratified by industry or disease. PMRs are presented for twenty-three site-specific cancers and 17 cardiovascular, neurodegenerative, respiratory, diabetes, renal and other diseases for up to fifteen industries in each of ten NORA sectors: agriculture, forestry, fishing; mining; oil and gas extraction; construction; manufacturing, wholesale & retail trade; transportation, warehousing & utilities; healthcare & social assistance; justice, public order and safety; and services.Users may consult the chart cause of death and industry category lists (shown in the next paragraph). These categories were structured in collaboration with National Occupational Research Agenda (NORA) Sector managers to help sector councils understand elevated mortality risks and identify gaps in knowledge that can help prioritize prevention. Users may consult the PMR Chart Cause of Death Categories and PMR Chart Sector Industry Categories listings.

Cause of Death, Industry and Occupation Lists
For deaths that occurred January 1, 1999 forward, the Tenth Revision ICD codes were used. A decedent’s usual occupation or industry was coded using 1990 U.S. Census codes between 1984 and 1993 and in 2000 U.S. Census codes from 1993 forward by most states (Industry Categories, Occupation Categories). Deaths that occurred during 1985-1998 are stratified on the basis of 280 Ninth Revision, International Classification of Diseases (ICD) coding categories (ICD Categories).

Statistical Analysis
The Proportionate Mortality Ratio Analysis System (PMRAS) 2011 system was designed to calculate PMRs by occupation or industry specifically for population-based data. It 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 stratification on race (white, Black) or ethnicity (Hispanic). Ninety-five or 99% percent confidence intervals (CI) are calculated on the expected deaths. A PMR above 100 is considered to exceed the average background risk for all occupations. The unemployed, part-time workers, 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 less than 5 deaths.

Although multiple comparisons are made in PMR analysis, statistical significance (p<0.05 for a two-sided test) and 95% CIs should be evaluated in the context of hypothesis generation [Rothman, 1986]. Although adjustment can be made for the large number of statistical comparisons, other criteria, such as statistical precision, previously published studies, and biologic plausibility, are usually drawn on when evaluating the observed PMR associations.

Interpretation
PMRs indicate whether the proportion of deaths due to a specific cause appears to be high or low for a particular occupation, compared to all other occupations. 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. The population at risk for this study includes all men and women employed usually in a specified occupation or industry, ages 15-90, who were at risk of dying at any time during the specified years of the analysis (1985-1998 or 1999, 2003-2004, 2007–2010). Because data by occupation and industry 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.

Limitations 
Limitations in the PMR method may bias risk estimates toward the null. 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. 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. 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. [Kircher 1985.]

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 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 impact of the healthy worker effect.