Others titles
- Cancer Deaths Among US Workers By Industry
- Cancer Proportionate Mortality Ratios By Decedents Industry
- Deaths Caused By Cancer Among US Workers By Industry
Keywords
- Cancer Mortality
- Cancer Deaths
- US Workers
- Industry Classification
- ICD-10 Codes
- Workers Age-Group
- Workers Gender
- Workers Race
- Proportionate Mortality Ratios
NIOSH Cancer Mortality Among US Workers By Industry
The dataset contains data for US workers who resided and died due to cancer during 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 cancer, gender and age-group of workers, as well as by the industry they worked in.
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Description
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 of 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 Myeloid Leukemia – C920
– Bladder Cancer (Includes in Situ) – C67
– Brain and Nervous System, All Neoplasms Except Secondary – C70-C72, D33, D42-D43
– Chronic Myeloid Leukemia – C921
– Hodgkin's Disease – C81
– Leukemia and Aleukemia – C910-C913, C915-C919, C92-C95
– Lymphatic Leukemia – C91
– Malignant Melanoma of Skin – C43
– Malignant Neoplasms (MN) – C00-C97
– MN Biliary Passages, Liver, And Gall Bladder – C22-C24
– MN Bladder – C67
– MN Bone and Articular Cartilage – C40-C41
– MN Bone, Connective Tissue, Skin, And Breast – C40-C44, C49-C50
– MN Brain – C71
– MN Brain and Nervous System – C47, C70-C72
– MN Breast – C50
– MN Cervix Uteri – C53
– MN Colon – C18
– MN Connective and Other Soft Tissue – C49, C461
– MN Digestive Organs and Peritoneum – C15-C26, C48
– MN Esophagus – C15
– MN Eye – C69
– MN Female Genital Organs – C51-C58
– MN Gallbladder and Extrahepatic Bile Ducts – C23-C24
– MN Kaposi Sarcoma (No Codes Before 1999) – C46
– MN Kidney – C64-C66
– MN Larynx – C32
– MN Lip – C00
– MN Lip, Oral Cavity and Pharynx – C00-C14, C462
– MN Lymphatic and Hematopoietic Tissue – C81, C463, C82-C85, C880, C883, C914, C96, C887, C889, C90, C910-C913, C915-C919, C92-C95
– MN Male Genital Organs – C60-C63
– MN Mesothelioma (No Codes Before 1999) – C45
– MN Nasal Cavities, Middle Ear and Accessory Sinuses – C30-C31
– MN Of Other and Unspecified Sites – C69-C80
– MN Other and Unspecified Female Genital Organs – C51-C52, C577, C579
– MN Other and Unspecified Urinary Organs – C68
– MN Other Parts Buccal Cavity – C03-C08, C462
– MN Other Parts of Uterus – C54-C55, C58
– MN Ovary and Other Uterine Adnexa – C56, C570-C574, C578
– MN Pancreas – C25
– MN Penis and Other Male Genital Organs – C60, C63
– MN Peritoneum & Other Digestive Organs – C26, C48
– MN Peritoneum and Pleura – C384, C48
– MN Pharynx – C09-C14
– MN Pleura (80% Mesothelioma in Males) – C384
– MN Prostate – C61
– MN Rectum, Rectosigmoid Junction and Anus – C19-C21
– MN Respiratory System – C30-C39
– MN Salivary Glands – C07-C08
– MN Scrotum – C632, C639
– MN Secondary, Ill-Defined and Unspecified Sites – C74-C80, C467-C468, C60, C64, C97
– MN Small Intestine, Including Duodenum – C17
– MN Stomach – C16
– MN Testis – C62
– MN Thyroid Gland and Other Endocrine Glands – C73
– MN Tongue – C01-C02
– MN Trachea, Bronchus and Lung – C33-C34
– MN Urinary Organs – C64-C68
– Multiple Myeloma – C887, C889, C90
– Non-Hodgkin's Lymphomas – C463, C82-C85, C880, C883, C914, C96
– Other Malignant Neoplasm of Skin – C44, C460, C469
About this Dataset
Data Info
Date Created | 2016-04-07 |
---|---|
Last Modified | 2018-10-15 |
Version | 2018-10-15 |
Update Frequency |
Irregular |
Temporal Coverage |
2007-2014 |
Spatial Coverage |
United States |
Source | John Snow Labs; Centers for Disease Control and Prevention (CDC), The National Institute for Occupational Safety and Health; |
Source License URL | |
Source License Requirements |
N/A |
Source Citation |
N/A |
Keywords | Cancer Mortality, Cancer Deaths, US Workers, Industry Classification, ICD-10 Codes, Workers Age-Group, Workers Gender, Workers Race, Proportionate Mortality Ratios |
Other Titles | Cancer Deaths Among US Workers By Industry, Cancer Proportionate Mortality Ratios By Decedents Industry, Deaths Caused By Cancer Among US Workers By Industry |
Data Fields
Name | Description | Type | Constraints |
---|---|---|---|
Industry | The industry type according to the 2000 US Census classification | string | required : 1 |
Industry_Census_Codes | The industry code according to the 2000 US Census classification | string | required : 1 |
Age_Group | The age group of decedents | string | enum : Array ( [0] => 18-64 years [1] => 18-90 years [2] => 65-90 years ) required : 1 |
Gender | The gender of the decedents | string | enum : Array ( [0] => Male [1] => Female [2] => Both genders ) required : 1 |
Race | The race of the decedents | string | enum : Array ( [0] => Black [1] => White [2] => Both races ) required : 1 |
Cause_Of_Death | One of the 61 cancer categories representing the underlying cause of death | string | required : 1 |
Cause_Of_Death_ICD_10_Codes | The ICD-10 Code/s of one of the 61 cancer categories representing the underlying cause of death | string | required : 1 |
Number_Of_Deaths | The number of deaths caused by one of the 61 cancer categories representing the underlying cause of death | integer | level : Ratio |
Proportionate_Mortality_Ratio | The proportionate mortality ratio for the corresponding deaths among workers from an industry having the specified demographic characteristics | integer | level : Ratio |
Lower_Confidence_Interval_Limit | The lower limit of the 95% confidence interval for proportionate mortality ratio value | integer | level : Ratio |
Upper_Confidence_Interval_Limit | The upper limit of the 95% confidence interval for proportionate mortality ratio value | integer | level : Ratio |
Significance_Level | The statistical significance level (p value) for proportionate mortality ratio value | string | - |
Data Preview
Industry | Industry Census Codes | Age Group | Gender | Race | Cause Of Death | Cause Of Death ICD 10 Codes | Number Of Deaths | Proportionate Mortality Ratio | Lower Confidence Interval Limit | Upper Confidence Interval Limit | Significance Level |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 65-90 years | Male | White | Malignant Neoplasms (MN) | C00-C97 | 23293 | 93 | 92 | 94 | p<0.01 |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-90 years | Male | White | Malignant Neoplasms (MN) | C00-C97 | 18963 | 94 | 93 | 95 | p<0.01 |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 65-90 years | Female | Black | Malignant Neoplasms (MN) | C00-C97 | 132 | 94 | 79 | 111 | |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-90 years | Female | Black | Malignant Neoplasms (MN) | C00-C97 | 190 | 95 | 82 | 110 | |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-64 years | Male | White | Malignant Neoplasms (MN) | C00-C97 | 3964 | 95 | 93 | 98 | p<0.01 |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-64 years | Female | Black | Malignant Neoplasms (MN) | C00-C97 | 58 | 99 | 75 | 128 | |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 65-90 years | Male | Black | Malignant Neoplasms (MN) | C00-C97 | 894 | 101 | 94 | 107 | |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 65-90 years | Female | White | Malignant Neoplasms (MN) | C00-C97 | 1608 | 104 | 100 | 109 | |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-90 years | Female | White | Malignant Neoplasms (MN) | C00-C97 | 2399 | 105 | 102 | 109 | p<0.01 |
Agriculture, Forestry, & Fisheries | 017-029, 748, 777 | 18-90 years | Male | Black | Malignant Neoplasms (MN) | C00-C97 | 1414 | 105 | 100 | 109 | p<0.05 |