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A retrospective cohort mortality study was conducted on 17,601 white and 4,722 nonwhite male workers in the Florida phosphate mining and chemical processing industry. Concerns about potential risks from naturally-occurring sources of ionizing radiation and anecdotal reports of lung cancer among workers prompted this investigation. Historical follow-up was conducted for the years 1949 to 1978. In comparison with U.S. rates, small excess of mortality rates of lung cancer were observed for white (standardized mortality ratio [SMR] equals 1.22) and nonwhite workers (SMR = 1.24); however, these excesses disappeared when contrasts were made with prevailing rates in Florida. Emphysema mortality was also slightly elevated in comparison with U.S. rates; the SMRs were 1.48 and 1.73 for white and nonwhite workers, respectively. Neither disease was related to overall length of employment. Detailed analyses of mortality in relation to work assignments are presented in the companion article.

A retrospective cohort mortality study was conducted in a population of workers employed at a facility with the primary task of production of nuclear fuels and other materials. Data for hourly and salaried employees were analyzed separately by time period of first employment and length of employment. The hourly (N = 6,687 with 728 deaths) and salaried (N = 2,745 with 294 deaths) employees had a mortality experience comparable to that of the United States and, in fact, exhibited significant fewer deaths in many categories of diseases that are traditionally associated with the healthy worker effect. Specifically, fewer deaths were noted in the categories of all causes, all cancers, cancer of the digestive organs, lung cancer, brain cancer (hourly workers only), diabetes, all diseases of the circulatory system, all respiratory diseases, all digestive system diseases, all diseases of the genitourinary system (hourly only), and all external causes of death. A statistically significant, and as yet unexplained increase in leukemia mortality (6 observed vs. 2.18 expected) appeared among a subset of the hourly employees, first hired before 1955, and employed between 5-15 years.

A mathematical model is used to predict relative body burdens of inhaled contaminants in workers who work two jobs or overtime, or who experience off-the-job exposure to air contaminants. Expected "peak" or maximum body burdens from multiple exposures are compared to those expected from the "normal" occupational exposure on which TLVs and Permissible Exposure Limits (PELs) arebased (five 8-hour days per week with zero off-the-job exposure). The model is designed to predict what adjustments to the TLVs and PELs are necessary to avoid accumulation of excess peak body burdens of contaminants from the additional exposures incurred. The general application of models to determine occupational exposure limits is reviewed, and several models are compared.

This paper analyzes current OSHA practice for adjusting the permissible exposure limit (PEL) for inorganic lead to accommodate unusual work schedules. Depending on the work schedule, the PEL adjustment procedure can allow substantially higher exposure or require substantially lower exposure, as compared to the exposure permitted by the lead standard for a normal work schedule. In addition, the procedure allows compliance with the standard by manipulation of the work schedule alone, without reduction in lead exposure. The paper describes how calculating exposure on a weekly basis using the procedure outlined in the OSHA Field Operations Manual would eliminate the seven-fold range of allowed exposure.

A previously reported model for predicting adjustments to permissible exposure limits has been extended to include adjustments for seasonal occupations. On the basis of the model, no "credit" (i.e., increase in exposure limits) may be allowed for a short work season unless the biological half-life in hours of the agent of concern is greater than 38 W, in which W is the number of weeks in the work season. Modifications to PELs due to the longer work-days and work-weeks often associated with seasonal occupations are incorporated into a general equation for estimating PEL adjustments. The adjustments assume no additional exposures the remainder of the year, and so do not apply to workers who merely move to other parts of the country and continue in a similar "seasonal" job.

The disease rate difference between people exposed and not exposed to some factor is the familiar estimate of attributable risk. When there are two or more causal exposures which demonstrate joint effects and are independently distributed, partitioning a population's overall rate between factors requires estimates of the marginal distributions of exposures and the independent (unconditional) and combined rate ratios for each exposure factor. The net potential reduction in the overall disease rate resulting from elimination of one of the exposures depends on these parameters and on the model of combined effect assumed, additive, multiplicative or some departure therefrom. In this paper the authors illustrate the extent to which the assumed model of combined effect of exposures determines which parameters need to be specified to estimate disease rate reductions. The concepts presented are demonstrated algebraically and with simulated hypothetical data. An application of the method is given with data from a case-control study of smoking, asbestos exposure and lung cancer.

A one-compartment exponential models is described which predicts "equal protection", based on biological uptake rates, for unusual air contaminant exposure situations as compared to a "normal" exposure of five 8-hour days per week. This is done by equating peak body burdens resulting from a normal and an unusual exposure, in terms of an adjustment factor applied to the normal exposure limit for an air contaminant. This factor predicts the highest allowable concentration in an unusual exposure situation which will not result in a higher-than normal body accumulation of the inhaled substance. Graphs and formulae are provided for determining adjustment factors for anticipated unusual exposure schedules. The model's predictions and published data are compared.