Methods     Glossary     References

These data were prepared for the third update of Occupational Mortality in Washington State, (1950-1971), published in 1976 by NIOSH.¹ The first update published in 1983 covered the years 1950-1979.² The second update covered the years 1950-1989 and was published in 1997.³ The current data tables cover the period from 1950-2010 and includes 1,053,769 male deaths occurring in 1950-2010 and 349,037 female deaths occurring in 1974-2010 (326,713 deaths to ‘housewives’ were not included in the analysis). Deaths to Washington residents age 20 or older are included in these data tables. About 94% of the decedents were classified as ‘white.’ The analysis includes deaths of Washington residents who died in other states or in Canada. Deaths that occurred in Washington to people who were residents of other states are not included.

There are two tables contained on this web site: male mortality by occupation for 1950-2010, and female mortality by occupation for 1974-2010. The tables include the number of observed and expected deaths, the PMR, and a p-value for eight age groups: 20-64, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+, and six time periods: 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2010.

Punch cards containing death record information were available for the years 1950-1971. Death certificate numbers of all deaths of Washington State male residents, age 20 or older, for the years 1950-1971 were listed in certificate number order within year of death. The death record occupation statements were abstracted and coded using a modification of the 1960 U.S. Census Bureau code. The death certificate numbers and the occupation codes were keyed into punch cards, and then matched to the original death cards on year and death certificate number. For matching cards, the occupation code was punched into the initial death card. Non-matches were resolved clerically. The completed cards were read onto magnetic tape.

For male deaths 1972-1986 and female deaths 1974-1986, occupation was manually coded on a monthly basis, and added to the routinely prepared computer record of death. These records were added to the existing 1950-1971 file. Occupational and industry literals were keyed into the death records from 1987-2010 and computer coded to occupation with a manual backup. At present, over 97% of death records are successfully computer coded to occupation, with less than 3% needing manual coding.

Since the study period includes four International Classification of Diseases (ICD) code changes, tenth, ninth and eighth revision codes were translated to seventh revision codes. The classification for causes of death given in this report are according to the seventh ICD revision with one exception; acquired immune deficiency syndrome (AIDS) is coded according to the ninth ICD (codes 042-044) starting with 1987 deaths. During the years 1983-1986, AIDS was coded to the ninth ICD code 279.1 (deficiency of cell-mediated immunity).

Age adjusted PMRs were calculated for the entire set of deaths, and for deaths to persons between 20 and 64 years old at death. Fourteen five-year age groups were used: 20–24, 25–29, ..., 85+. In addition to these PMRs, age-specific and calendar time period-specific PMRs were calculated in order to allow a detailed examination of the association between occupation and cause-of-death. To compute the PMRs, deaths were tabulated as in the following typical table for a specific age group:

When both the observed number of deaths and the expected number of deaths were greater than 5, the Mantel-Haenszel chi-square test statistic was used to test the null hypothesis that the PMR = 100, and to calculate a p-value for the test. The Mantel-Haenszel chi-square test statistic is calculated as follows:⁴

and the p-value is taken from the chi-square distribution.

When either the observed or expected number of deaths was 5 or less, a binomial test was used where the p value was the 2-sided binomial probability, under the null hypothesis, of obtaining the observed number or a number more extreme, relative to the expected number of deaths.⁵

A proportional mortality ratio is the proportion of deaths in a specific occupation that are due to a specific cause-of-death divided by the proportion of deaths in all occupations that are due to that specific cause of death. For example, among male school teachers there were a total of 7,778 deaths, and 332 of these were caused by lung cancer. There were a total of 811,256 deaths to males of all occupations, of which 46,802 were caused by lung cancer. So the crude PMR for lung cancer among male school teachers is (332/7778)/(46,802/811,256) X 100 = 74. (In the data reported on this website, crude PMRs are not used, instead, the PMRs are age-adjusted to account for different age distributions in different occupations.) A PMR greater than 100 indicates that members of that occupation were more likely than average to die of that cause-of-death, while a PMR of less than 100 indicates that they were less likely than average to die of that cause-of-death.

PMRs may also be calculated by dividing the observed number of deaths by the expected number of deaths in an occupation and cause-of-death group.

The greatest value of proportional mortality ratios is that they can be used in studies where mortality rates cannot be calculated, because of a lack of information on the population at risk. The greatest weakness of PMRs is that they say nothing about the overall force of mortality.⁶ A proportional excess in one cause of death may mean either an excess in the rate for that cause of death, or a deficit in the rates of other causes. In a healthy cohort, a deficit of cardiovascular diseases can cause a proportional excess of some cancers, even when there is no excess in the absolute rate of those cancers. Several researchers have compared the performance of PMRs to that of standardized mortality ratios (SMRs), and studied the effect of the `healthy worker effect' on PMRs. Decouflé and colleagues show that a cause-specific PMR is equivalent to the cause-specific SMR multiplied by the overall SMR for the cohort.⁷ Therefore, the PMR will either over-estimate or under-estimate the corresponding SMR if the overall SMR is either less than or greater than 100.

Correct interpretation of the results of PMR studies, therefore, requires an awareness of the weaknesses of the technique, and a consideration of the epidemiologic factors operating on each occupation.

The number of deaths that actually occurred in a specific occupation and cause-of-death group.

The age-adjusted number of deaths that would have occurred in a specific occupation and cause-of-death group, if that occupation had the same mortality experience as the entire cohort.

A measure of the statistical significance of the PMR. All the p-values in this analysis are two-sided.

¹ Milham S (1976). Occupational Mortality in Washington State, 1950-1971. DHEW Publication No.(NIOSH) 76-175-A,B,C.

² Milham S (1983). Occupational Mortality in Washington State, 1950-1979. DHEW Publication No. (NIOSH) 83-116, 1983.

³ Milham S (1997). Occupational Mortality in Washington State 1950-1989. DHHS Publication No.96-133.

⁴ Breslow NE and Day NE (1980). Statistical Methods in Cancer Research: Volume I- The Analysis of Case Control Studies, International Agency for Research on Cancer, Lyon.

⁵ Ross S (1988). A First Course in Probability. MacMillan Publishing Co. New York, third edition.

⁶ Milham S (1975). Methods in occupational mortality studies. J Occ Med 17(9):581-585.

⁷ Decouflé P, Thomas TL, Pickle LW (1980). Comparison of the proportionate mortality ratio and standardized mortality ratio risk estimates. Am J Epi 111(3):263-269.