NIOSH Cardiovascular Mortality Among US Workers By Industry

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The dataset contains data for US workers who resided and died due to a cardiovascular disease during the periods 2007-2010, in one of 25 US States. Mortality described through proportionate mortality ratios (PMRs) along with the number of deaths is described by the type of cardiovascular disease, gender and age-group of workers, as well as by the industry they worked in.


The source of data is provided by The National Institute for Occupational Safety and Health (NIOSH), through the National Occupational Mortality Surveillance (NOMS). NIOSH mission is to develop new knowledge in the field of occupational safety and health and to transfer that knowledge into practice.

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 or ethnicity. 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. 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 included all men and women employed usually in a specified occupation or industry, ages 18-90, who were at risk of dying at any time during the specified years of the analysis (2007–2010). The unemployed, part-time workers, students, volunteers, and those in unknown occupations or industries (less than three percent), were excluded from the analysis. The Tenth Revision ICD codes were used for deaths and the decedent’s industry was coded using the 2000 U.S. Census codes. PMR statistics are suppressed for any occupations or industries with less than 5 deaths. 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, proportionate mortality based on cumulative deaths over the time period studied was evaluated. PMRs were calculated for White and Black, males and females, and all races and genders combined to evaluate the mortality patterns. The 95% confidence intervals (95% CIs) were computed based on the Poisson distribution if the observed number of deaths was 1000 or less; otherwise, test-based CIs were computed based on the Mantel and Haenszel chi square test. 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. Because the number of deaths under 5 were suppressed during the analysis and the exact number for deaths in these cases were not given (being defined as “<5") the values were removed.

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 dataset lacks information on the length of employment, specificity of the 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. 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.

The hearth diseases categories, representing the underlying cause of death (and the corresponding ICD-10 Codes, are the following:

– Acute Myocardial Infarction (Ami) – I21

– Cardiomegaly – I42

– Cardiomyopathy – I42, I528

– Cerebrovascular Disease (Stroke) – G450-G452, G454-G459, I60-I69

– Chronic Disease Of Endocardium – I34-I38

– Chronic Pulmonary Heart Disease – I279

– Conductive Disorders, Dysrhythmias – I44-I49, R001, R008

– Dilated Cardiomyopathy – I420

– Diseases Of Arteries, Arterioles, And Capillaries – I70-I79

– Diseases Of The Heart – I00-I52, I970-I971, I978-I979

– Diseases Of The Pulmonary Circulation – I26-I28

– Diseases Of Veins And Lymphatics – I80-I89

– Hypertension With Heart Disease – I11, I13

– Hypertension Without Heart Disease – I10, I12

– Ischemic Heart Disease – I20-I22, I24-I25, I513, I516

– Other Forms Of Heart Disease – I30-I52

– Other Heart Disease (Pericarditis, Endocarditis, Myocarditis, Etc.) – I30-I33 , I40 , I50 , I510-I512, I514-I515, I517-I521, I970-I971, I978-I979

– Raynaud'S Syndrome – I730

– Rheumatic Heart Disease & Fever – I00-I09

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Last Modified




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Spatial Coverage

United States


John Snow Labs; Centers for Disease Control and Prevention (CDC), The National Institute for Occupational Safety and Health;

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Source License Requirements


Source Citation

NIOSH (2015). National Occupational Mortality Surveillance (NOMS). U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluation and Field Studies, Surveillance Branch. Date accessed.2017-09.


Cardiovascular Mortality, Cardiovascular Deaths, US Workers, Industry Classification, ICD-10 Codes, Workers Age-Group, Workers Gender, Workers Race, Proportionate Mortality Ratios

Other Titles

Cardiovascular Deaths Among US Workers By Industry, Cardiovascular Proportionate Mortality Ratios By Decedents Industry, Deaths Caused By Cardiovascular Diseases Among US Workers By Industry

IndustryThe industry type according to the 2000 US Census classificationstringrequired : 1
Industry_Census_CodesThe industry code according to the 2000 US Census classificationstringrequired : 1
Age_GroupThe age group of decedentsstringenum : Array required : 1
GenderThe gender of the decedentsstringenum : Array required : 1
RaceThe race of the decedentsstringenum : Array required : 1
Cause_Of_DeathOne of the 19 cardiovascular disease categories representing the underlying cause of deathstringrequired : 1
Cause_Of_Death_ICD_10_CodesThe ICD-10 Code/s of one of the 19 cardiovascular disease categories representing the underlying cause of deathstringrequired : 1
Number_Of_DeathsThe number of deaths caused by one of the 19 cardiovascular disease categories representing the underlying cause of deathnumberlevel : Ratio
Proportionate_Mortality_RatioThe proportionate mortality ratio for the corresponding deaths among workers from an industry having the specified demographic characteristicsintegerlevel : Interval
Lower_Confidence_Interval_LimitThe lower limit of the 95% confidence interval for proportionate mortality ratio valueintegerlevel : Ratio
Upper_Confidence_Interval_LimitThe upper limit of the 95% confidence interval for proportionate mortality ratio valueintegerlevel : Ratio
Significance_LevelThe statistical significance level (p value) for proportionate mortality ratio valuestringenum : Array
Data_AnalysisIndicates the analysis method mentioned in description; "Suppressed" values indicate at the same time that the corresponding number of deaths were removedstringrequired : 1
Navy96965-90 yearsMaleBlackCardiomegalyI42Suppressed
Navy96918-90 yearsMaleBlackCardiomegalyI42Suppressed
Navy96918-64 yearsMaleBlackCardiomegalyI42Suppressed
Army96718-64 yearsFemaleWhiteCardiomegalyI42Suppressed
Army96718-64 yearsFemaleBlackCardiomegalyI42Suppressed
Army96765-90 yearsFemaleBlackCardiomegalyI42Suppressed
Army96718-90 yearsFemaleBlackCardiomegalyI42Suppressed
Army96718-90 yearsFemaleWhiteCardiomegalyI42Suppressed
Army96765-90 yearsFemaleWhiteCardiomegalyI42Suppressed
Navy96918-64 yearsFemaleWhiteCardiomegalyI42Suppressed