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. 2018 Aug 18;6:195–244. doi: 10.1016/j.ssmph.2018.08.002

Table 2.

Empirical studies of blue-collar women's health, organized by health outcome category (n = 177)*.

Outcome Category Author (Year) Title Country Years Observed Sample Size (N) Women (N) Blue-Collar Women (N) Industry Subsector
BMI & metabolism Melamed et al. (1995) Objective and subjective work monotony: effects on job satisfaction, psychological distress, and absenteeism in blue-collar workers Israel 1985–1987 1278 393 393 Manufacturing
Nakamura, Nakamura, and Tanaka (2000) Increased risk of coronary heart disease in Japanese blue-collar workers Japan 1993 1145 492 492 Computer and Electronic Product Manufacturing
Santos and Barros (2003) Prevalence and determinants of obesity in an urban sample of Portuguese adults Portugal NR 1424 868 254 Multiple Industries
Maty et al. (2005) Education, income, occupation, and the 34-year incidence (1965 -99) of Type 2 diabetes in the Alameda County Study United States 1965–1999 6147 3293 417 Multiple Industries
Bennett, Wolin, and James (2007) Lifecourse socioeconomic position and weight change among Blacks: the Pitt County Study United States 1988–2001 1167 751 573 Multiple Industries
Forman-Hoffman et al. (2008) Retirement and weight changes among men and women in the Health and Retirement Study United States 1994–2002 3725 1759 994 Multiple Industries
Yang et al. (2008) Emergence of socioeconomic inequalities in smoking and overweight and obesity in early adulthood: the National Longitudinal Study of Adolescent Health United States 1995–1996, 2001–2002 9542 4580 NR Multiple Industries
Cho and Lee (2012) The relationship between cardiovascular disease risk factors and gender South Korea 2005 4556 2596 NR Multiple Industries
Duffy et al. (2012) Predictors of Obesity in Michigan Operating Engineers United States 2008 498 37 37 Specialty Trade Contractors
Eshak et al. (2013) Soft drink, 100% fruit juice, and vegetable juice intakes and risk of diabetes mellitus Japan 1990–2000 27585 15448 6565 Multiple Industries
Miura and Turrell (2014) Reported consumption of takeaway food and its contribution to socioeconomic inequalities in body mass index Australia 2009 903 480 40 Multiple Industries
Lewin et al. (2014) Residential neighborhood, geographic work environment, and work economic sector: associations with body fat measured by electrical impedance in the RECORD study France 2007–2008 4331 NR NR Multiple Industries
Hwang and Lee (2014) Effect of psychosocial factors on metabolic syndrome in male and female blue-collar workers South Korea 2010 234 80 80 Chemical Manufacturing; Computer and Electronic Product Manufacturing; Fabricated Metal Product Manufacturing; Transportation Equipment Manufacturing;
Cancer van Loon, Goldbohm, and van den Brandt (1994) Socioeconomic status and breast cancer incidence: a prospective cohort study Netherlands 1986–1989 1716 1716 457 Multiple Industries
van Loon, van den Brandt, and Golbohm (1995) Socioeconomic status and colon cancer incidence: a prospective cohort study Netherlands 1986–1989 3658 1871 494 Multiple Industries
Cocco, Dosemeci, and Heineman (1998) Occupational risk factors for cancer of the central nervous system: a case-control study on death certificates from 24 U.S. States United States 1984–1992 142,080 64,900 NR Multiple Industries
Pollán & Gustavsson (1999) High-risk occupations for breast cancer in the Swedish female working population Sweden 1971–1989 1,101,669 1,101,669 NR Multiple Industries
Richardi et al. (2004) Occupational risk factors for lung cancer in men and women: a population-based case-control study in Italy Italy 1990–2002 2724 476 476 Multiple Industries
Thompson et al. (2005) Occupational exposure to metalworking fluids and risk of breast cancer among female autoworkers United States 1941–1994 4680 4680 4680 Transportation Equipment Manufacturing
Hrubá et al. (2009) Socioeconomic indicators and risk of lung cancer in Central and Eastern Europe Czech Republic, Hungary, Poland, Romania, Slovakia, Russia, and the United Kingdom 1998–2001 5979 1469 617 Multiple Industries
Colt et al. (2011) Occupation and bladder cancer in a population-based case-control study in Northern New England United States 2001–2004 2560 634 47 Multiple Industries
Betenia, Costello, and Eisen (2012) Risk of cervical cancer among female autoworkers exposed to metalworking fluids United States 1985–2004 4374 4374 4374 Transportation Equipment Manufacturing
Oddone et al. (2013) Female breast cancer in Lombardy, Italy (2002 - 2009): a case-control study on occupational risks Italy 2002–2009 78349 78349 36517 Multiple Industries
Pudrovska et al. (2013) Higher-status occupations and breast cancer: a life-course stress approach United States 1951–2011 3682 3682 NR Multiple Industries
Oddone et al. (2014) Female breast cancer and electrical manufacturing: results of a nested case-control study Italy 2002–2009 216 216 145 Computer and Electronic Product Manufacturing
Cardiovascular disease Zhao et al. (1991) A dose response relation for noise induced hypertension China 1985 1101 1101 1101 Textile Product Mills
Hall, Johnson, and Tsou (1993) Women, occupation, and risk of cardiovascular morbidity and mortality Sweden 1977, 1979, 1980, 1981 5921 5921 NR Multiple Industries
Hammar, Alfredsson, and Theorell (1994) Job characteristics and the incidence of myocardial infarction Sweden 1970, 1985, 1976–1981, 1976–1984. 35396 4667 2283 Multiple Industries
Melamed et al. (1995) Objective and subjective work monotony: effects on job satisfaction, psychological distress, and absenteeism in blue-collar workers Israel 1985–1987 1278 393 393 Manufacturing
Jousilahti et al. (1996) Symptoms of chronic bronchitis and the risk of coronary disease Finland 1972–1985, 1977–1990 19444 10102 766 Multiple Industries
Melamed et al. (1997) Industrial noise exposure, noise annoyance e, and serum lipid levels in blue-collar workers--the CORDIS study Israel NR 2079 624 624 Manufacturing
Wamala et al. (1997) Lipid profile and socioeconomic status in health middle aged women in Sweden Sweden 1991–1994 300 300 64 Multiple Industries
Östlin et al. (1998) Myocardial infarction in male and female dominated occupations Sweden 1969–1970, 1970–1990, 1971–1992, 1976–1984 140520 36708 NR Multiple Industries
Baigi, Marklund, and Fridlund (2001) The association between socio-economic status and chest pain focusing on self-rated health in a primary health care area of Sweden Sweden NR 1145 492 404 Multiple Industries
Tsutsumi et al. (2001) Association between job strain and prevalence of hypertension: a cross sectional analysis in a Japanese working population with a wide range of occupations: the Jichi Medical School cohort study Japan 1992–1994 6587 3400 1931 Multiple Industries
Wamala, Lynch, and Kaplan (2001) Women's exposure to early and later life socioeconomic disadvantage and coronary heart disease risk: the Stockholm Female Coronary Risk Study Sweden 1991–1994 585 177 177 Multiple Industries
Gallo et al. (2003) Occupation and subclinical carotid artery disease in women: are clerical workers at greater risk? United States 1983–1985 362 362 27 Multiple Industries
Honjo et al. (2010) Socioeconomic indicators and cardiovascular disease among Japanese community residents: The Jichi Medical School Cohort Study Japan 1992–2005 10640 6511 2084 Multiple Industries
Clougherty et al. (2011) Gender and sex differences in job status and hypertension United States 1996–2002 14618 2016 793 Primary Metal Manufacturing; Fabricated Metal Product Manufacturing
Tsutsumi, Kayaba, and Ishikawa (2011) Impact of occupational stress on stroke across occupational classes and genders Japan 1992–2005 6553 3363 1867 Multiple Industries
Cho and Lee (2012) The relationship between cardiovascular disease risk factors and gender South Korea 2005 4556 2596 NR Multiple Industries
Stokholm et al. (2013) Occupational noise exposure and the risk of hypertension Denmark 2001–2007 145190 36788 15728 Multiple Industries
Won et al. (2013) Actual cardiovascular disease risk and related factors: a cross-sectional study of Korean blue-collar workers employed by small businesses South Korea 2010 238 82 82 NR
Fujishiro et al. (2015) Occupational characteristics and the progression of carotid artery intima-media thickness and plaque over 9 years: the Multi-Ethnic Study of Atherosclerosis (MESA) United States 2000–2011 3109 1610 166 Multiple Industries
Disability & absenteeism Arber (1991) Class, paid employment and family roles: making sense of structural disadvantage, gender and health status United Kingdom 1985–1986 26060 13283 NR Multiple Industries
Guendelman and Silberg (1993) The health consequences of maquiladora work: women on the US-Mexican border Mexico 1990 480 480 241 Computer and Electronic Products Manufacturing; Apparel Manufacturing; Accommodation and Food Services
Vahtera et al. (1999) Workplace as an origin of health inequalities Finland 1991–1993 2793 1875 NR Multiple Industries
Korda et al. (2002) The Health of the Australian workforce: 1998-2001 Australia 1998–2001 9167 4107 595 Multiple Industries
Aittomäki, Lahelma, and Roos (2003) Work conditions and socioeconomic inequalities in work ability Finland 2000 1827 1398 161 Multiple Industries
Väänänen et al. (2004) Role clarity, fairness, and organizational climate as predictors of sickness absence: a prospective study in the private sector Finland 1995–1998 3850 937 385 Forestry and Logging
Strong & Zimmerman (2005) Occupational injury and absence from work among African American, Hispanic, and non-Hispanic White workers in the National Longitudinal Survey of Youth United States 1988–2000 35710 16839 1890 Multiple Industries
Christensen et al. (2008) Explaining the social gradient in long-term sickness absence: a prospective study of Danish employees Denmark 2000–2002 5221 2562 671 Multiple Industries
Niedhammer et al. (2008) The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey France 2003 24468 10245 1409 Multiple Industries
Väänänen et al. (2008) Work-family characteristics as determinants of sickness absence: a large-scale cohort study of three occupational grades Finland 2000–2002 18366 13971 1802 Multiple Industries
von Bonsdorff et al. (2011) Work ability in midlife as a predictor of mortality and disability in later life: a 28-year prospective follow-up study Finland 1981–2009 5971 3261 1692 Multiple Industries
Gupta et al. (2014) Face validity of the single work ability item: comparison with objectively measured heart rate reserve over several days Denmark NR 127 53 53 Multiple Industries
Heo et al. (2015) Job stress as a risk factor for absences among manual workers: a 12-month follow-up study South Korea 2009–2010 2349 542 542 Manufacturing
Health behaviors Burton and Turrell (2000) Occupation, hours worked, and leisure-time physical activity Australia 1995 24454 11029 1972 Multiple Industries
Wu and Porell (2000) Job characteristics and leisure physical activity United States 1992 6443 2881 871 Multiple Industries
Gang et al. (2002) Physical activity during leisure and commuting in Tianjin, China China 1996 3976 1974 809 Multiple Industries
Takao et al. (2003) Occupational class and physical activity among Japanese employees Japan 1996–1998 20,654 3,017 1585 Computer and Electronic Products Manufacturing; Fabricated Metal Product Manufacturing; Primary Metal Manufacturing; Transportation Equipment Manufacturing
McCormack, Giles-Corti, and Milligan (2006) Demographic and individual correlates of achieving 10,000 steps/day: use of pedometers in a population-based study Australia NR 428 223 19 Multiple Industries
Ericson et al. (2007) Dietary intake of heterocyclic amines in relation to socioeconomic, lifestyle, and other dietary factors: estimates in a Swedish population Sweden 1991–1994 490 490 43 Multiple Industries
Kuiack, Irving, and Faulkner (2007) Occupation, hours worked, caregiving, and leisure time physical activity Canada 2000 490 490 43 Multiple Industries
Harley et al. (2010) Multiple health behavior changes in a cancer prevention intervention for construction workers, 2001 - 2003 United States 2002–2003 582 17 17 Construction of Buildings
Mäkinen et al. (2010) Occupational class differences in leisure-time physical inactivity - contribution of past and current physical workload and other working conditions Finland 2000 3355 1788 273 Multiple Industries
Cleland et al. (2011) Correlates of pedometer-measured and self-reported physical activity among young Australian adults Australia 2004–2006 2017 923 NR Multiple Industries
Cho and Lee (2012) The relationship between cardiovascular disease risk factors and gender South Korea 2005 4556 2596 NR Multiple Industries
Miura and Turrell (2014) Reported consumption of takeaway food and its contribution to socioeconomic inequalities in body mass index Australia 2009 903 480 40 Multiple Industries
Oliveira, Maia, and Lopes (2014) Determinants of inadequate fruit and vegetable consumption amongst Portuguese adults Portugal 1999–2003 2362 1455 NR Multiple Industries
Uijtdewilligen et al. (2014) Biological, socio-demographic, work and lifestyle determinants of sitting in young adult women: a prospective cohort study Australia 2000, 2003, 2006, 2009 11676 11676 NR Multiple Industries
Hwang et al. (2015) Predictors of health-promoting behavior associated with cardiovascular diseases among Korean blue-collar workers South Korea NR 234 80 80 NR
Uijtdewilligen et al. (2015) Determinants of physical activity in a cohort of young adult women. Who is at risk of inactive behaviour? Australia 2000, 2003, 2006, 2009 11695 11695 NR Multiple Industries
Mental health Loscocco & Spitze (1990) Working conditions, social support, and the well-being of female and male factory workers United States 1982 2222 649 649 Multiple Industries
Parkinson et al. (1990) Health effects of long-term solvent exposure among women in blue-collar occupations United States NR 567 567 567 Computer and Electronic Product Manufacturing
Bromet et al. (1992) Effects of occupational stress on the physical and psychological health of women in a microelectronics plant United States NR 552 552 552 Computer and Electronic Product Manufacturing
Guendelman and Silberg (1993) The health consequences of maquiladora work: women on the US-Mexican border Mexico 1990 480 480 241 Computer and Electronic Products Manufacturing; Apparel Manufacturing; Accommodation and Food Services
Melamed et al. (1995) Objective and subjective work monotony: effects on job satisfaction, psychological distress, and absenteeism in blue-collar workers Israel 1985–1987 1278 393 393 Manufacturing
Kivimäki and Kalimo (1996) Self-esteem and the occupational stress process: testing two alternative models in a sample of blue-collar workers Finland NR 5450 927 927 NR
Goldenhar et al. (1998) Stressors and adverse outcomes for female construction workers United States NR 211 211 211 Construction of Buildings
Rydstedt, Johansson, and Evans (1998) A longitudinal study of workload, health and well-being among male and female urban drivers Sweden 1991–1992 56 32 32 Transit and Ground Passenger Transportation
Soares, Grossi, and Sundin (2007) Burnout among women: associations with demographic/socioeconomic, work, life-style and health factors Sweden NR 6000 6000 745 Multiple Industries
Andrés, Collings, and Qin (2009) Sex-specific impact of socio-economic factors on suicide risk: a population-based case-control study in Denmark Denmark 1981–1997 328608 109410 19922 Multiple Industries
Cohidon et al. (2009) Mental health of workers in Toulouse 2 years after the industrial AZF disaster: first results of a longitudinal follow-up of 3,000 people France 2003–2008 2847 1514 53 Multiple Industries
Asztalos et al. (2009) Specific associations between types of physical activity and components of mental health Belgium 2002–2004 1919 901 140 Multiple Industries
Brunette, Smith, and Punnett (2011) Perceptions of working and living conditions among industrial male and female workers in Perú Perú 2002 1066 305 305 Multiple Industries
Moon and Park (2011) Risk factors for suicidal ideation in Korean middle-aged adults: the role of socio-demographic status South Korea 2005 7301 4087 991 Multiple Industries
Ahlgren, Olsson, and Brulin (2012) Gender analysis of musculoskeletal disorders and emotional exhaustion: interactive effects from physical and psychosocial work exposures and engagement in domestic work Sweden 2008 1373 515 253 Food Manufacturing; Professional, Scientific and Technical Services
Minh (2014) Work-related depression and associated factors in a shoe manufacturing factory in Haiphong City, Vietnam Vietnam 2012 420 327 227 Leather and Allied Product Manufacturing
Yoon et al. (2014) Occupational noise annoyance linked to depressive symptoms and suicidal ideation: a result from nationwide survey of Korea South Korea 2007–2009 10020 4610 1934 Multiple Industries
Mortality Hall, Johnson, and Tsou (1993) Women, occupation, and risk of cardiovascular morbidity and mortality Sweden 1977, 1979, 1980, 1981 5921 5921 NR Multiple Industries
Pekkanen et al. (1995) Social class, health behaviour, and mortality among men and women in Eastern Finland Finland 1970, 1972, 1975, 1977–1987 18661 9694 6376 Multiple Industries
Chenet et al. (1998) Deaths from alcohol and violence in Moscow: socio-economic determinants Russia 1994–1995 86121 22619 NR Multiple Industries
Arena et al. (1999) Issues and findings in the evaluation of occupational risk among women high nickel alloys workers United States 1948–1988 2877 2877 2877 Primary Metal Manufacturing
Kareholt (2001) The relationship between heart problems and mortality in different social classes Sweden 1968, 1974, 1981, 1991, 1992, 1968–1996 4585 2285 1170 Multiple Industries
Baigi et al. (2002) Cardiovascular mortality focusing on socio-economic influence: the low-risk population f Halland compared to the population of Sweden as a whole Sweden 1980–1990 3247211 1592467 1250828 Multiple Industries
Prescott et al. (2003) Social position and mortality from respiratory diseases in males and females Denmark 1976, 1978, 1981–1983, 1992–1993, 1964–1992 29392 13992 NR Multiple Industries
Akerstedt, Kecklund, and Johansson (2004) Shift work and mortality Sweden 1979–2000 22411 8401 4163 Multiple Industries
Mamo et al. (2005) Factors other than risks in the workplace as determinants of socioeconomic differences in health in Italy Italy 1981–2001 377828 136212 NR Multiple Industries
Bentley et al. (2007) Area disadvantage, individual socio-economic position, and premature cancer mortality in Australia 1998 to 2000: a multilevel analysis Australia 1998–2000 5998961 2602424 382266 Cross-Sectional
Hein et al. (2007) Follow-up study of chrysotile textile workers: cohort mortality and exposure-response United States 1916–2001 3072 1265 1256 Textile Product Mills
Lipton, Cunradi, and Chen (2008) Smoking and all-cause mortality among a cohort of urban transit operators United States 1983–2000 1785 161 161 Transit and Ground Passenger Transportation
Brockmann, Müller, and Helmert (2009) Time to retire - time to die? A prospective cohort study of the effects of early retirement on long-term survival Germany 1990–2004 129675 41276 26803 Multiple Industries
von Bonsdorff et al. (2011) Work ability in midlife as a predictor of mortality and disability in later life: a 28-year prospective follow-up study Finland 1981–2009 5971 3261 1692 Multiple Industries
Dasgupta et al. (2012) Multilevel determinants of breast cancer survival: association with geographic remoteness and area-level socioeconomic disadvantage Australia 1997–2006 18568 18568 715 Multiple Industries
von Bonsdorff et al. (2012) Job strain among blue-collar and white-collar employees as a determinant of total mortality: a 28-year population-based follow-up Finland 1981–2009 5731 3261 1688 Multiple Industries
Hirokawa et al. (2013) Mortality risks in relation to occupational category and position among the Japanese working population: the Jichi Medical School (JMS) cohort study Japan 1992–2005 6929 3596 1524 Multiple Industries
Mattisson, Horstmann, and Bogren (2014) Relationship of SOC with sociodemographic variables, mental disorders, and mortality Sweden 1947, 1957, 1972, 1997–2011 1164 625 325 Multiple Industries
Costello et al. (2014) Social disparities in heart disease risk and survivor bias among autoworkers: an examination based on survival models and g-estimation United States 1941–1995 39412 4797 4797 Transportation Equipment Manufacturing
Zhang et al. (2015) Occupation and risk of sudden death in a United States community: a case-control analysis United States 2006–2013 1268 332 62 Multiple Industries
Musculoskeletal Vingard et al. (1991) Occupation and osteoarthrosis of the hip and knee: a register-based cohort study Sweden 1960, 1970, 1980, 1981–1983 250217 42549 42549 Multiple Industries
Westgaard and Jansen (1992) Individual and work related factors associated with symptoms of musculoskeletal complains. II Different risk factors among sewing machine operators Norway NR 245 245 210 Textile Product Mills
Iverson and Erwin (1997) Predicting occupational injury: the role of affectivity Australia NR 362 65 65 Manufacturing
Fredriksson et al. (1999) Risk factors for neck and upper limb disorders: results from 24 years of follow-up Sweden 1969–1993 484 252 37 Multiple Industries
Kaergaard and Andersen (2000) Musculoskeletal disorders of the neck and shoulders in female sewing machine operators: prevalence, incidence and prognosis Denmark 1994–1997 243 243 243 Textile Product Mills
Murata, Kawakami, and Amari (2000) Does job stress affect injury due to labor accident in Japanese male and female blue-collar workers? Japan 1989–1999 168 76 63 Chemical Manufacturing
Björkstén et al. (2001) Reported neck and shoulder problems in female industrial workers: the importance of factors at work and at home Sweden NR 173 173 173 Fabricated Metal Product Manufacturing; Food Manufacturing
Khatun, Ahlgren, and Hammarström (2004) The influence of factors identified in adolescence and early adulthood on social class inequities of musculoskeletal disorders at age 30: a prospective population-based cohort study Sweden 1981–1995 1044 497 NR Multiple Industries
Kaila-Kangas et al. (2006) How consistently distributed are the socioeconomic differences in severe back morbidity by age and gender? A population based study of hospitalisation among Finnish employees Finland 1995–1996 1517897 773936 193088 Multiple Industries
Nakata et al. (2006) The prevalence and correlates of occupational injuries in small-scale manufacturing enterprises Japan 2002 1298 385 138 Manufacturing
Pollack et al. (2007) Use of employer administrative databases to identify systematic causes of injury in aluminum manufacturing United States 2002–2004 9101 835 835 Primary Metal Manufacturing
Wang et al. (2007) Work-organisational and personal factors associated with upper body musculoskeletal disorders among sewing machine operators United States 2003–2005 520 335 335 Textile Product Mills
Niedhammer et al. (2008) The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey France 2003 24468 10245 1409 Multiple Industries
Taiwo et al. (2008) Sex differences in injury patterns among workers in heavy manufacturing United States 1996–2005 9527 692 692 Primary Metal Manufacturing
Roquelaure et al. (2008) Work increases the incidence of carpal tunnel syndrome in the general population France 2002–2004 1168 819 194 Multiple Industries
Kim et al. (2009) Depressive symptoms and self-reported occupational injury in small and medium-sized companies South Korea 2006–2007 1350 501 404 Manufacturing
Mattioli et al. (2009) Risk factors for operated carpal tunnel syndrome: a multicenter population-based case-control study Italy 1997–1998, 2001 477 401 172 Multiple Industries
Roquelaure et al. (2009) Attributable risk of carpal tunnel syndrome in the general population: implications for intervention programs in the workplace France 2002–2004 388078 194276 24090 Multiple Industries
Nag, Vyas, and Nag (2010) Gender differences, work stressors, and musculoskeletal disorders in weaving industries India 2007 516 263 263 Textile Product Mills
Brunette, Smith, and Punnett (2011) Perceptions of working and living conditions among industrial male and female workers in Perú Perú 2002 1066 305 305 Multiple Industries
Motamedzade and Moghimbeigi (2011) Musculoskeletal disorders among female carpet weavers in Iran Iran NR 626 626 626 Textile Product Mills
Ahlgren, Olsson, and Brulin (2012) Gender analysis of musculoskeletal disorders and emotional exhaustion: interactive effects from physical and psychosocial work exposures and engagement in domestic work Sweden 2008 1373 515 253 Food Manufacturing; Professional, Scientific and Technical Services
Andersen et al. (2012) Cumulative years in occupation and the risk of knee osteoarthritis in men and women: a register-based follow-up study Denmark 1981–2006 2117298 1100979 38485 Construction of Buildings
Lombardo et al. (2012) Musculoskeletal symptoms among female garment factory workers in Sri Lanka Sri Lanka NR 1058 1058 1000 Apparel Manufacturing
Kubo et al. (2013) Associations between employee and manager gender: impacts on gender-specific risk of acute occupational injury in metal manufacturing United States 2002–2007 2645 2322 2322 Primary Metal Manufacturing; Fabricated Metal Product Manufacturing
Lipscomb, Schoenfisch, and Cameron (2013) Work-related injuries involving a hand or fingers among union carpenters in Washington state, 1989 - 2008 United States 1989–2008 24,830 646 646 Specialty Trade Contractors
Hanklang et al. (2014) Musculoskeletal disorders among Thai women in construction-related work Thailand 2011 272 272 272 Specialty Trade Contractors
Tessier-Sherman (2014) Occupational injury risk by sex in a manufacturing cohort United States 2001–2010 23956 5063 5063 Primary Metal Manufacturing; Fabricated Metal Product Manufacturing
Cantley et al. (2015) Expert ratings of job demand and job control as predictors of injury and musculoskeletal disorder risk in a manufacturing cohort United States 2004–2005 9260 946 946 Primary Metal Manufacturing; Fabricated Metal Product Manufacturing
Hallman et al. (2015) Association between objectively measured sitting time and neck-shoulder pain among blue-collar workers Denmark 2011–2012 202 84 84 Multiple Industries
Other Parkinson et al. (1990) Health effects of long-term solvent exposure among women in blue-collar occupations United States NR 567 567 567 Computer and Electronic Product Manufacturing
Bromet et al. (1992) Effects of occupational stress on the physical and psychological health of women in a microelectronics plant United States NR 552 552 552 Computer and Electronic Product Manufacturing
Grimmer (1993) Relationship between occupation and episodes of headache that match cervical origin pain patterns Australia NR 417 202 42 Multiple Industries
Tsai et al. (1997) Neurobehavioral effects of occupational exposure to low-level organic solvents among Taiwanese workers in paint factories Taiwan 1992–1993 298 85 32 Chemical Manufacturing
Goldenhar, Swanson, & Hurrell (1998) Stressors and adverse outcomes for female construction workers United States NR 211 211 211 Construction of Buildings
Nguyen et al. (1998) Noise levels and hearing ability of female workers in a textile factory in Vietnam Vietnam NR 69 69 69 Textile Mills
Rydstedt, Johansson, and Evans (1998) A longitudinal study of workload, health and well-being among male and female urban drivers Sweden 1991–1992 56 32 32 Transit and Ground Passenger Transportation
Juutilainen et al. (2000) Nocturnal 6-hydroxymelatonin sulfate excretion in female workers exposed to magnetic fields Finland NR 60 60 39 Apparel Manufacturing
Shirom, Melamed, and Nir-Dotan (2000) The relationships among objective and subjective environmental stress levels and serum uric acid: the moderating effect of perceived control Israel 1985–1987 3680 1176 1176 Manufacturing
Korda et al. (2002) The Health of the Australian workforce: 1998-2001 Australia 1998–2001 9167 4107 595 Multiple Industries
Kovacevic and Belojevic (2006) Tooth abrasion in workers exposed to noise in the Montenegrin Textile Industry Serbia NR 225 225 111 Textile Mills
Potula and Kaye (2006) The impact of menopause and lifestyle factors on blood and bone lead levels among female former smelter workers: the Bunker Hill Study United States 1994, 2000 73 73 NR Primary Metal Manufacturing
Cobankara et al. (2011) The prevalence of fibromyalgia among textile workers in the city of Denizli in Turkey Turkey 2005 655 523 523 Textile Mills
Choi et al. (2013) Factors associated with sleep quality among operating engineers United States 2008 498 37 37 Specialty Trade Contractors
Lin et al. (2015) Risk for work-related fatigue among the employees on semiconductor manufacturing lines Taiwan 2007 1545 428 428 Computer and Electronic Product Manufacturing
Reproductive & sexual health Eskenazi, Guendelman, and Elkin (1993) A preliminary study of reproductive outcomes of female maquiladora workers in Tijuana, Mexico Mexico 1990 360 360 241 Computer and Electronic Products Manufacturing; Apparel Manufacturing; Accommodation and Food Services
Luoto, Kaprio, and Uutela (1994) Age at natural menopause and sociodemographic status in Finland Finland 1989 1505 1505 511 Multiple Industries
Evans et al. (2003) Predictors of seropositivity to herpes simplex virus type 2 in women United Kingdom 1992 520 520 88 Multiple Industries
Gissler et al. (2009) Trends in socioeconomic differences in Finnish perinatal health 1991 - 2006 Finland 1991–2006 931285 931285 154359 Multiple Industries
Jakobsson and Mikoczy (2009) Reproductive outcome in a cohort of male and female rubber workers: a registry study Sweden 1973–2001 NR NR NR Plastics and Rubber Products Manufacturing; Food Manufacturing
Lalive & Zweimüller (2009) How does parental leave affect fertility and return to work? Evidence from two natural experiments Austria 1985, 1987, 1990, 1993, 1996 6180 6180 NR Multiple Industries
Sakr et al. (2010) Reproductive outcomes among male and female workers at an aluminum smelter United States 2006 419 76 38 Primary Metal Manufacturing
Sayem et al. (2010) An assessment of risk behaviours for HIV/AIDS among young female garment workers in Bangladesh Bangladesh 2007 300 300 300 Apparel Manufacturing
Yingying, Smith, and Suiming (2011) Changes and correlates in multiple sexual partnerships among Chinese adult women--population based surveys in 2000 and 2006 China 2000, 2006 4525 4525 922 Multiple Industries
del Bono, Weber, and Winter-Ebmer (2012) Clash of career and family: fertility decisions after job displacement Austria 1990–1998 227199 227199 NR Multiple Industries
Pant et al. (2013) Knowledge of and attitude towards HIV/AIDS and condom use among construction workers in the Kathmandu Valley, Nepal Nepal 2013 317 33 33 Construction of Buildings
Räisänen et al. (2014) Influence of delivery characteristics and socioeconomic status on giving birth by caesarean section - a cross sectional study during 2000–2010 in Finland Finland 2000–2010 620463 620463 90032 Multiple Industries
von Ehrenstein et al. (2014) Preterm birth and prenatal maternal occupation: the role of Hispanic ethnicity and nativity in a population-based sample in Los Angeles, California United States 2003 2543 2,543 186 Multiple Industries
Wang et al. (2015) Sulfur dioxide exposure and other factors affecting age at natural menopause in the Jinchuan cohort China 2012 3167 3167 2657 Primary Metal Manufacturing
Respiratory Kongerud and Soyseth (1991) Methacholine responsiveness, respiratory symptoms, and pulmonary function in aluminum potroom workers Norway 1988 337 38 38 Primary Metal Manufacturing
Love et al. (1991) The characteristics of respiratory ill health of wool textile workers United Kingdom NR 620 145 145 Textile Mills
Raza et al. (1999) Ventilatory function and personal breathing zone dust concentrations in Lancashire textile weavers United Kingdom NR 302 NR NR Textile Mills
Seldén et al. (2001) Exposure to tremolite asbestos and respiratory health in Swedish dolomite workers Sweden 1996 130 16 16 Mining (except oil and gas); Nonmetallic Mineral Product Manufacturing
Takezaki et al. (2001) Dietary factors and lung cancer risk in Japanese: with special reference to fish consumption and adenocarcinomas Japan 1988–1997 5198 1486 178 Multiple Industries
Bakirci et al. (2007) Natural history and risk factors of early respiratory responses to exposure to cotton dust in newly exposed workers Turkey NR 157 74 74 Textile Mills
Heikkilä et al. (2008) Asthma incidence in wood-processing industries in Finland in a register based population study Finland 1986–1998 170963 25148 16937 Wood Product Manufacturing; Forestry and Logging
Thilsing et al. (2012) Chronic rhinosinusitis and occupational risk factors among 20- to 75-year-old Danes--a GA2LEN-based study Denmark 2008 2531 1331 550 Multiple Industries
Storaas et al. (2015) Incidence of rhinitis and asthma related to welding in Northern Europe Iceland, Norway, Sweden, Denmark, Estonia 1990–1994, 1999–2001 16191 8398 219 Fabricated Metal Product Manufacturing
Wang et al. (2015) Synergistic impaired effect between smoking and manganese dust exposure on pulmonary ventilation function in Guangxi Manganese-Exposed Workers Healthy Cohort (GXMEWHC) China 2011–2012 1658 620 620 Primary Metal Manufacturing
Self-Rated health Korda et al. (2002) The Health of the Australian workforce: 1998-2001 Australia 1998–2001 9167 4107 595 Multiple Industries
Niedhammer et al. (2008) The contribution of occupational factors to social inequalities in health: findings from the national French SUMER survey France 2003 24468 10245 1409 Multiple Industries
Brunette, Smith, and Punnett (2011) Perceptions of working and living conditions among industrial male and female workers in Perú Perú 2002 1066 305 305 Multiple Industries
Hammarström, Stenlund, and Janlert (2011) Mechanisms for the social gradient in health: results form a 14-year follow-up of the Northern Swedish Cohort Sweden 1981–1995 1083 495 NR Multiple Industries
Landefeld et al. (2014) The association between a living wage and subjective social status and self-rated health: a quasi-experimental study in the Dominican Republic Dominican Republic 2011 204 134 134 Apparel Manufacturing
Smoking & Other Substance Use Cunradi, Lipton, and Banerjee (2007) Occupational correlates of smoking among urban transit operators: a prospective study United States 1983–1985; 1993–1995 654 54 54 Transit and Ground Passenger Transportation
Radi, Ostry, and LaMontagne (2007) Job stress and other working conditions: relationships with smoking behaviors in a representative sample of working Australians Australia NR 1101 575 74 Multiple Industries
Yang et al. (2008) Emergence of socioeconomic inequalities in smoking and overweight and obesity in early adulthood: the National Longitudinal Study of Adolescent Health United States 1995–1996, 2001–2002 9542 4580 NR Multiple Industries
Okechukwu, Nguyen, and Hickman (2010) Partner smoking characteristics: associations with smoking and quitting among blue-collar apprentices United States NR 1767 88 88 Construction of Buildings
Sayem et al. (2010) An assessment of risk behaviours for HIV/AIDS among young female garment workers in Bangladesh Bangladesh 2007 300 300 300 Apparel Manufacturing
Biron, Bamberger, and Noyman (2011) Work-related risk factors and employee substance use: insights from a sample of Israeli blue-collar workers Israel NR 569 NR NR Manufacturing
Hammarström et al. (2011) Mechanisms for the social gradient in health: results form a 14-year follow-up of the Northern Swedish Cohort Sweden 1981–1995 1083 495 NR Multiple Industries
Chin et al. (2012) Cigarette smoking in building trades workers: the impact of work environment United States 2004–2007 1817 88 88 Construction of Buildings
Chin et al. (2012) Occupational factors and smoking cessation among unionized building trades workers United States 2004– 2007 763 44 44 Construction of Buildings
Chin et al. (2013) Heavy and light/moderate smoking among building trades construction workers United States NR 763 63 63 Construction of Buildings
Cho and Lee (2012) The relationship between cardiovascular disease risk factors and gender South Korea 2005 4556 2596 NR Multiple Industries
Fujishiro et al. (2012) Occupational gradients in smoking behavior and exposure to workplace environmental tobacco smoke: the Multi-Ethnic Study of Atherosclerosis (MESA) United States 2000–2002 6355 3249 373 Multiple Industries
Noonan and Duffy (2012) Smokeless tobacco use among operating engineers United States 2008 498 37 37 Specialty Trade Contractors
Okechukwu et al. (2012) Smoking among construction workers: the nonlinear influence of the economy, cigarette prices, and antismoking sentiment United States 1992–1993, 1995–1996, 1998–1999, 2001–2002, 2003, 2006–2007 52418 1479 1479 Construction of Buildings
Cunradi, Ames, and Xiao (2014) Binge drinking, smoking and marijuana use: the role of women's labor force participation United States 2006–2007 956 956 104 Construction of Buildings
Maron et al. (2015) Occupational inequalities in psychoactive substance use: a question of conceptualization Germany 2012 9084 5155 994 Multiple Industries
Outcome Category Study Design Independent Variable(s)** Specific Outcome Referent Group Summary of Study Findings Brief Description of Study Population Country Classification Citation
BMI & metabolism Cross-Sectional Age, sex, education, ethnic origin, repetitive work (short, medium, and long-cycle), work underload, subjective monotony Serum glucose levels Exposure-outcome among blue-collar women Among blue-collar women, short-cycle repetitive work was associated with higher serum glucose (p = 0.05) levels. Cardiovascular Occupational Risk Factors Determination in Israel Study (CORDIS) High-Income (Melamed et al., 1995)
Cross-Sectional Occupational class, marital status Waist circumference, waist-to-hip ratio Blue-collar women vs. other women Among blue-collar women, waist circumference (ß = -2.40, SE = 8.19) and waist to hip ratio (ß = -0.0294, SE = 0.0065) were lower as compared with white-collar women. Employees from a single Japanese computer and printing manufacturing company High-Income (Nakamura et al., 2000)
Cross-Sectional Age, education, occupational class, marital status, smoking status, regular physical exercise, physical activity tertiles, total energy intake quartiles Obesity Blue-collar women vs. other womenC The odds of obesity among blue-collar women were increased as compared with white-collar women (OR = 3.5, 95% CI 2.21– 5.55). Adults living in Porto, Portugal recruited with random digit-dialing High-Income (Santos & Barros, 2003)
Longitudinal Education, log-income, occupational class Type 2 Diabetes Blue-collar women vs. other women The hazard of type 2 diabetes among blue-collar women was increased as compared with white-collar women (HR = 0.86, 95% CI 0.53 –1.41). Alameda County Study High-Income (Maty et al., 2005)
Longitudinal Parental occupation; childhood household deprivation (public assistance, no plumbing, no electricity, food scarcity); adult SEP index; education; occupational class; lifecourse SEP Weight Change Blue-collar women vs. other womenC Blue-collar women demonstrated larger 13- year increases in BMI as compared with white-collar women (5.8 vs. 4.8 kg/m2, p 0.05). Pitt County Study High-Income (Bennett et al., 2007)
Longitudinal Recent Retirement Weight loss of 5% or greater; weight gain of 5% or greater Exposure-outcome among blue-collar women Among blue-collar women, retirement was inversely associated with a weight loss of 5% or greater (OR = 0.88, 95% 0.57–1.37). Retirement was positively associated with a weight gain of 5% or greater (OR = 1.58, 95% CI 1.13–2.21). Health and Retirement Study (HRS) High-Income (Forman-Hoffman et al., 2008)
Longitudinal Young adult socioeconomic position (education and occupational class); family socioeconomic position; family structure; family connectedness; smoker in home; easy access to cigarettes; high school; CES-D; number of friends who smoke; BMI during adolescence Overweight, Obesity Blue-collar women vs. other womenC Odds of overweight (OR = 1.04, 0.49–2.21) or obesity (OR = 0.74, 0.29 –1.85) were not increased among blue-collar women as compared to women with further education. National Longitudinal Study of Adolescent Health High-Income (Yang et al., 2008)
Cross-Sectional Occupational class, education, poverty-income ratio Obesity Blue-collar women vs. other womenC The odds of obesity among blue-collar women were increased as compared with white-collar women (OR = 2.08, 95% CI = 1.43–3.05). Third Korean National Health and Nutrition Examination Survey (KNHANES III) High-Income (Cho and Lee, 2012)
Cross-Sectional Age, female, white, married, high-school or less, SF-36 pain, self-reported medical comorbidities, depression, smoking, alcohol problem, vegetable intake, fruit intake, fried food intake, physical activity Obesity Male vs. female blue-collar workersC The odds of obesity among female operating engineers were decreased as compared with male operating engineers (OR = 0.283, 95% CI 0.083–0.827). Convenience sample of operating engineers recruited during a three-day safety training course High-Income (Duffy et al., 2012)
Longitudinal Soft drinks intake, 100% fruit juice intake, vegetable juice intake, age, sports activity, education, occupational class, BMI, Menopausal status Diabetes Mellitus Exposure-outcome among blue-collar women Among blue-collar women, the odds of type 2 diabetes were increased among those who drank soda every day (OR = 2.57, 95% CI 1.25–5.29); those who drank soda 3-4 times per week (OR = 125, 95% CI 0.66–2.35); and those who drank soda two times or less per week (OR = 1.18, 95% CI 0.77–1.80) as compared with those who rarely drank soda. Japan Public Health Center Study (JPHC) High-Income (Eshak et al., 2013)
Cross-Sectional Education, household income, occupational class Healthy takeaway food; less healthy takeaway food Blue-collar women vs. other women Blue-collar women had increased BMI as compared with professionals and managers (ß = 2.83, SE = 0.99). Adults randomly selected form the electoral roll of the Brisbane statistical subdivision. High-Income (Miura and Turrell, 2014)
Cross-Sectional Age, individual education, parental education, HDI of country of birth, residential education level, residential density of population, occupational class, home work distance Fat Mass Index Blue-collar women vs. other women There was no significant association between work economic sector and fat mass index (FMI) for women in the manufacturing industry (ß = 0.31, 95% CI 0.04–1.73); construction (ß = 0.18, 95% CI - 2.44 to 2.81); or commercial repair of motor vehicles and motorcycles (ß = 0.43, 95%CI - 0.60 to 1.46) as compared with women in transport and communications. Residential Environment and Coronary Heart Disease Cohort Study (RECORD) High-Income (Lewin et al., 2014)
Cross-Sectional Overtime work (≥ 60 hours/week), social support, job stress, risk perception, physical exercise (≥ 30 min, ≥ 3 per week) Metabolic Syndrome Exposure-outcome among blue-collar women Among blue-collar women, low job stress (OR = 0.05, p = 0.04), low social support (OR = 1.51, p = 0.009), and risk perception (OR = 1.27, p = 0.023) were associated with metabolic syndrome. Blue-collar workers at small companies recruited from occupational health centers or worksites during annual health checkups in South Korea. High-Income (Hwang and Lee, 2014)
Cancer Longitudinal Highest level of education, highest level of education (household), EPG score: last profession, U&S score: last profession Breast Cancer Blue-collar women vs. other women There was no difference in breast cancer risk in blue-collar women and lower white-collar women (RR = 1.01, 95% CI 0.69–1.49); the risk of breast cancer was increased among upper white-collar women as compared with blue-collar women (RR = 1.19, 95% CI 0.80 –1.76). The Netherlands Cohort Study (NLCS) High-Income (Van Loon et al., 1994)
Longitudinal Highest level of education, occupational class, social standing (U&S) score Colon Cancer Blue-collar women vs. other women As compared with blue-collar women, the risk of colon cancer was increased among lower white-collar women (RR = 1.30, 95% CI 0.76–2.22) but decreased among upper white-collar women (RR = 0.63, 95% CI 0.30–1.29) The Netherlands Cohort Study (NLCS) High-Income (Van Loon et al., 1995)
Case-Control Occupation Cancer of the CNS Blue-collar women vs. other women Industries showing consistent increases in risk for cancer of the CNS by gender and race included textile mills, paper mills, printing and publishing industries, petroleum refining, motor vehicles manufacturing, telephone and electric utilities, department stores, health care services, elementary and secondary schools, and colleges and universities. United States Vital Statistics Records High-Income (Cocco et al., 1999)
Longitudinal Occupation Breast Cancer Blue-collar women vs. other women Excess risk for breast cancer was found for pharmacists, teachers of theoretical subjects, schoolmasters, systems analysts and programmers, telephone operators, telegraph and radio operators, metal platers and coaters, and hairdressers and beauticians. All living Swedish women ages 25–64 who were employed at the time of the 1970 census and present in the country during the 1960 census. High-Income (Pollán and Gustavsson, 1999)
Case-Control Exposure to known and suspected lung carcinogens. Lung Cancer Exposure-outcome among blue-collar women Lung cancer risk was increased among female rubber workers exposed to suspected carcinogens versus those unexposed (OR = 2.2, 95% CI = 0.6–7.9); among female glass workers exposed to suspected carcinogens versus those unexposed (OR = 2.8, 95% CI 0.4–22); and among laundry and dry cleaners exposed to suspected carcinogens versus those unexposed (OR = 2.1, 95% CI = 0.8–5.6). Incident lung cancer cases and population controls in Northern Italy High-Income (Richiardi et al., 2004)
Case-Control Metalworking fluid (MWF) Breast Cancer Exposure-outcome among blue-collar women There was an increase in the odds of breast cancer associated with every mg/m3-year increase of cumulative exposure to soluble MWF over the ten-year study period (OR = 1.18, 95% CI 1.02–1.35). Female hourly automobile production workers from three large manufacturing plants in Michigan High-Income (Thompson et al., 2005)
Case-Control Occupational class, education Lung Cancer Blue-collar women vs. other women The odds of lung cancer among blue-collar women were decreased as compared with white-collar women (OR = 0.96, 95% CI 0.74–1.25). Incident lung cancer cases and hospital-based controls in seven Eastern European countries. High-Income and Upper-Middle-Income (Hrubá et al., 2009)
Case-Control Occupation of first employment Bladder Cancer Exposure-outcome among blue-collar women Among women, bladder cancer risk was significantly elevated and increased significantly with duration of employment in the electronic components and accessories industry (OR = 2.2, 95% CI 1.1 to 4.7) and the transportation equipment industry (OR = 8.7, 95% CI 2.0–37). Incident bladder cancer cases and population controls in Maine, Vermont, and New Hampshire. High-Income (Colt et al., 2011)
Longitudinal Metalworking fluid (MWF) Cervical Cancer Exposure-outcome among blue-collar women There was no difference in cervical cancer risk among blue-collar women exposed or not exposed to straight MWF (RR = 1.0, 95% CI 0.46–2.19). Risk of cervical cancer among blue-collar women exposed to soluble MWF increased as compared with unexposed workers (RR = 1.55, 95% CI 0.66–3.61). Risk of cervical cancer among blue-collar women exposed to synthetic MWF increased as compared with unexposed women (RR = 1.14, 95% CI 0.50–2.60). Female hourly automobile production workers from three large manufacturing plants in Michigan High-Income (Betenia et al., 2012)
Case-Control Duration of Employment Breast Cancer Exposure-outcome among blue-collar women The odds of breast cancer among women with 20+ years of employment versus those with 0 to 4 years of employment were increased in the iron and steel industry (OR = 1.22, 95% CI 0.72–2.07); mechanical manufacturing (OR = 1.11, 95% CI 0.92–1.34); electrical manufacturing (OR = 1.37, 95% CI 1.10–1.71); the food industry (OR = 1.13, 95% CI 0.83–1.19); the textile industry (OR = 1.13, 95% CI 0.98–1.29); the garment industry (OR = 1.16, 95% CI 1.01–1.34); the wood industry (OR = 1.22, 95% CI 0.80–1.85); the rubber industry (OR = 2.71, 95% CI 1.25–5.87); the building industry (OR = 1.45, 95% CI 0.28–7.59); the transport industry (OR = 1.15, 95% CI 0.34–3.93); the chemical industry (OR = 1.52, 95% CI 0.96–2.42); the alcoholic beverages and wine production industry (OR = 1.46, 95% CI 0.26–8.10); the pharmaceutical industry (OR = 1.31, 95% CI 0.70–2.43); and the dry cleaning sector (OR = 2.29, 95% CI 0.97–5.41) but not for women in healthcare and veterinarian services, the plastic industry, the pottery industry, agriculture, the paper industry, the leather and shoe industry, or the press industry. Incident cases of female breast cancer and population controls in Lombardy, Italy High-Income (Oddone et al., 2013)
Longitudinal Occupational class in 1993 and 1975; high job authority in 1975; adiposity in 1957; reproductive history in 1975 and 1993; job characteristics in 1975; health behaviors in 1993; work under pressure of time, responsibility outside control, high job autonomy; job satisfaction; high job authority; life-course estrogen cycle; family history of breast cancer Breast Cancer Blue-collar women vs. other women The risk of breast cancer was increased among female crafts/operatives laborers as compared with housewives (HR = 0.87, 95% CI 0.51–1.48). Wisconsin Longitudinal Survey (WLS) High-Income (Pudrovska et al., 2013)
Case-Control Exposure to lead and lead alloys, chlorinated solvents, lubricant oils, non-ionizing radiation, epoxy resins, and job title Breast Cancer Blue-collar women vs. other women The odds of breast cancer were increased among blue-collar women exposed to chlorinated solvents as compared with unexposed women (OR 1.65, 95% CI 1.04−2.62). There was a two-fold increase among blue-collar women exposed for at least 10 years as compared with unexposed women (OR 2.10, 95% CI 1.21−3.66). Incident cases of female breast cancer and controls selected from a single, large electrical manufacturing plant near Milan, Italy. High-Income (Oddone et al., 2014)
Cardiovascular disease Cross-Sectional Sound pressure level, age, working years, salt (high), salt (normal), family history Hypertension Exposure-outcome among blue-collar women Among female textile mill workers, sound pressure levels (SPL) were associated with the prevalence of hypertension (ß = 0.03, SE = 0.015). Female workers in a textile mill in Beijing, China Upper-Middle-Income (Zhao et al., 1991)
Longitudinal Occupational class, work control, work social support, psychological job demand, physical job demand Cardiovascular morbidity Exposure-outcome among blue-collar women Among blue-collar women, cardiovascular morbidity was more prevalent among those with low work social support (OR = 1.19, 95% CI 1.01–1.14) and high physical job demand (OR = 1.15, 95% CI 0.97–1.35). Cardiovascular disease was less prevalent among blue-collar women with high psychological job demand (OR = 0.76, 95% CI = 0.60–0.97). There was no association between work control and cardiovascular morbidity (OR = 1.02, 95% CI 087–1.20). Survey of Living Conditions High-Income (Hall et al., 1993)
Case-Control Job characteristics Myocardial Infarction Exposure-outcome among blue-collar women Among blue-collar women, increased risk of first MI was associated with monotony (RR = 1.4, 95% CI 0.3–6.9) few possibilities to learn new things (RR = 2.1, 95% CI = 0.9–4.9), long working hours (RR = 1.1, 95% CI 0.7–1.7), low influence on planning of work (RR = 2.0, 95% CI = 0.3–15.6), low influence on working hours (RR = 1.1, 95% CI 0.8–1.7), and noise (RR = 1.4, 95% CI 0.9–2.1). Decreased risk of MI was associated with hectic work (RR = 0.7, 95% CI 0.5–1.1) and low influence on work tempo (RR = 0.7, 95% CI 0.4–1.3) Incident cases of myocardial infarction and population-based controls in four rural Swedish Counties and Stockholm County. High-Income (Hammar et al., 1994)
Cross-Sectional Age, sex, education, ethnic origin, repetitive work (short, medium, and long-cycle), work underload, subjective monotony Systolic blood pressure, diastolic blood pressure, total cholesterol, LDL, HDL, triglycerides Exposure-outcome among blue-collar women Among blue-collar women, short-cycle repetitive work was associated with higher mean systolic (p = 0.003) and diastolic (p = 0.01) blood pressure; and total cholesterol (p = 0.03). Cardiovascular Occupational Risk Factors Determination in Israel Study (CORDIS) High-Income (Melamed et al., 1995)
Longitudinal Symptoms of chronic bronchitis First coronary event Exposure-outcome among blue-collar women Among blue-collar women, the risk of first coronary event was increased among those with Grade 1 symptoms (RR = 1.98, 95% CI 0.56–7.01) and those with Grade 2 symptoms (RR = 1.93, 95% CI 0.69–5.39) as compared with blue-collar women with no symptoms. A random sample of the population of the eastern Finnish provinces of North Karelia and Kuopio. High-Income (Jousilahti et al., 1996)
Cross-Sectional Noise-exposure level, noise annoyance Cholesterol, LDL, HDL, Cholesterol/HDL, triglycerides Exposure-outcome among blue-collar women Among blue-collar women with high noise-exposure and high noise-annoyance, the mean-adjusted cholesterol level was 207 mg/dl (SE = 9.4); LDL levels were 125 mg/dl (SE = 8.6); HDL levels were 57 mg/dl (SE = 3.1); the ratio of Cholesterol to HDL was 4.1 (SE = 0.3) and the mean-adjusted triglyceride level was 126 mg/dl (SE = 14.0). Cardiovascular Occupational Risk Factors Determination in Israel Study (CORDIS) High-Income (Melamed et al., 1997)
Case-Control Education, occupational level, decision latitude at work Cholesterol, triglycerides, HDL, Cholesterol/HDL, LDL/HDL, ApoB, ApoA1 Blue-collar women vs. other women As compared with white-collar women, cholesterol levels (difference = 0.11, p = 0.42), triglyceride levels (difference = 0.07, p = 0.78), HDL levels (difference = 0.09, p = 0.23), the cholesterol to HDL ratio (difference = 0.36, p = 0.17), the LDL to HDL ratio (difference = 0.31, p = 0.18), and the Apolipoprotein B to apolipoprotein A1 ratio (difference = 0.06, p = 0.63) were higher among blue-collar women. Stockholm Female Coronary Risk Study (FemCorRisk) High-Income (Wamala et al., 1997)
Case-Control Occupational class, male- or female-dominated occupation Myocardial Infarction Blue-collar women vs. other women Increased risk of MI was found among blue-collar women (RR = 1.41, 95%CI 1.15–1.73) in jobs where men predominate as compared with other women. Population aged 30-74 residing in one of five Swedish counties including Stockholm. High-Income (Ostlin et al., 1998)
Cross-Sectional Age, occupational class Pain or discomfort in the chest when excited; pain or discomfort in the chest after a substantial meal; Palpitation of the heart or irregular heartbeat Blue-collar women vs. other women As compared with blue-collar women, white-collar women experienced less pain or discomfort in the chest when excited (OR = 0.42, 95% CI 0.26–0.68). There was no difference in the odds of pain or discomfort in the chest after a substantial meal (OR = 1.05, 95% CI 0.45–2.44) or palpitations of the heart or irregular heartbeat (OR = 0.99, 95% CI 0.71–1.38). A stratified sample of residents aged 18 to 74 in the four primary health care areas of Halland County, Sweden. High-Income (Baigi et al., 2001)
Cross-Sectional Job strain Hypertension Exposure-outcome among blue-collar women There was no association between job strain and hypertension among blue-collar women (OR = 1.01, 95% CI 0.87–1.17). The Jichi Medical School Cohort Study (JMS) High-Income (Tsutsumi et al., 2001)
Case-Control Short stature, early life socioeconomic disadvantage (large early life family size, singletons, born last, low education), adult life socioeconomic disadvantage (occupational class at labour force entry, blue-collar occupation at examination, economic hardship prior to CHD event). Coronary heart disease Blue-collar women vs. other women The odds of CHD were increased among women whose occupation at labor force entry was blue-collar as compared with women whose occupation at labor force entry was white-collar (OR = 1.80, 95% CI 1.12–3.12). The odds of CHD among women whose occupation at examination was blue-collar as compared to women whose occupation at examination was white-collar (OR = 1.69, 95% CI 0.95–2.88). Stockholm Female Coronary Risk Study (FemCorRisk) High-Income (Wamala et al., 2001)
Cross-Sectional Age, behavioral risk factors, occupational class Average carotid intima-media thickness Blue-collar women vs. other women Carotid intima-media thickness was reduced among blue-collar women as compared with female clerical workers (ß = -0.064, SE = 0.027). Healthy Women Study (HWS) High-Income (Gallo et al., 2003)
Longitudinal Occupational class Total stroke, intraparanchymal hemorrhage, subarachnoid hemorrhage, ischemic stroke, coronary heart disease Blue-collar women vs. other women As compared with blue-collar women, white-collar women had lower risk of total stroke (HR = 0.93, 95% CI 0.58–1.51), intraparenchymal hemorrhage (HR = 0.34, 95% CI 0.09–1.21), ischemic stroke (HR = 0.72, 95% CI 0.36–1.47), and coronary heart disease (HR = 0.66, 95% CI 0.20–2.21). Risk of subarachnoid hemorrhage was increased among white-collar women compared with blue-collar women (HR = 2.68, 1.03–6.94). The Jichi Medical School Cohort Study (JMS) High-Income (Honjo et al., 2010)
Longitudinal Occupational class Hypertension Blue-collar women vs. other women Among women, there was an association between hourly (i.e. blue-collar) status and hypertension among those predicted to be hourly workers based on propensity scores (OR = 1.78, 95% CI 1.34–2.35). The American Manufacturing Cohort Study (AMC) High-Income (Clougherty et al., 2011)
Longitudinal Job characteristics Stroke Exposure-outcome among blue-collar women Among blue-collar women, there was no association between risk of incident stroke among women with active jobs (HR = 0.9, 95% CI 0.3–24), passive jobs (HR = 1.0, 95% CI 0.4–2.4), or high strain jobs (HR = 1.04, 95% CI 0.4–2.5) as compared to those with low-strain jobs. The Jichi Medical School Cohort Study (JMS) High-Income (Tsutsumi et al., 2011)
Cross-Sectional Occupational class, education, poverty-income ratio Hypertension, non-HDL Cholesterol Blue-collar women vs. other women The odds of hypertension among blue-collar women were increased (OR = 1.30, 95% CI = 1.04–1.61) and the odds of NHDLC were decreased (OR = 0.74, 95% CI = 0.51–1.09) as compared with white-collar women. Third Korean National Health and Nutrition Examination Survey (KNHANES III) High-Income (Cho and Lee, 2012)
Longitudinal Cumulative noise exposure, duration of exposure, first year of exposure Hypertension Blue-collar women vs. other women The risk of hypertension among female industrial workers is increased as compared with female financial workers (RR = 1.17, 95% CI 1.09–1.26). Workers employed in one of 625 companies in the industrial trades and 100 companies in the financial services in Aarhaus County, Denmark. High-Income (Stokholm et al., 2013)
Cross-Sectional Age, gender, education, knowledge of CVD risk, CVD risk perception, waist-to-hip ratio, social support, ERI ratio (job stress), exposure to chemicals or noise, shift work, overtime work Actual CVD Risk Male vs. female blue-collar workersC Actual cardiovascular disease risk among blue-collar women was decreased as compared with blue-collar men (ß = - 0.092, p = 0.709). Blue-collar workers from companies with fewer than 300 employees recruited through an occupational health center in South Korea. High-Income (Won et al., 2013)
Longitudinal Occupational class Annual and baseline differences in common carotid intima-media thickness (IMT), carotid plaque score, and prevalence of carotid plaque showing. Blue-collar women vs. other women Compared with professional women at baseline, the common carotid IMT was increased (0.005, 95% -0.026 to 0.035), the carotid plaque score was decreased (-0.04, 95% CI - 22.7 to 28.3) and the prevalence of carotid plaque showing was decreased (-12.6, 95% CI - 41.7 to 31.2) in blue-collar women. Compared with professionals, the annual change in common carotid IMT was smaller (-0.001, 95% CI - 0.003 to 0.002), the annual change in carotid plaque score was greater (0.03, 95% CI - 2.0 to 2.7) and the annual change in the prevalence of carotid plaque showing was greater (2.0, 95% CI - 2.9 to 7.1) in blue-collar women. Multi-Ethnic Study of Atherosclerosis (MESA) High-Income (Fujishiro et al., 2015)
Disability & Absenteeism Cross-Sectional Occupational class and material circumstances; unemployment and labor force participation; and family roles Limiting Long-Standing Illness Blue-collar women vs. other women The odds of limiting, longstanding illness were increased among unskilled manual (OR = 2.24, p < 0.05); semi-skilled manual (OR = 1.70, p < 0.05); and skilled manual women (OR = 1.79, p < 0.05) as compared with professional women. British General Household Survey High-Income (Arber, 1991)
Cross-Sectional Occupation, months on the job, decision latitude, social supports at work, work dissatisfaction, does not have enough money, economic tensions, family tensions, negative self-image, perceived health status, age, education, number of children < 15 years, number of utilities Functional impediments Blue-collar women vs. other women Female electronics workers reported fewer functional impediments than service workers (ß = - 0.72, 95% CI - 1.39, to 0.03) and female garment workers reported fewer functional impediments than service workers (ß = - 0.21, 95% CI - 0.84,0.42). Women in Tijuana working as electronic maquiladora workers; garment maquiladora workers; service workers; and those with no labor force history Upper-Middle-Income (Guendelman and Silberg, 1993)
Longitudinal Occupational class Short periods of sick leave; long periods of sick leave; sick leave because of an infection; sick leave because of musculoskeletal disorder; sick leave because of trauma Blue-collar women vs. other women As compared with higher grade white-collar women, the risk of short periods of sick leave (RR = 1.13, 95% CI 0.99 - 1.29), long periods of sick leave (RR = 2.80, 95% CI 2.55–3.06), sick leave because an infection (RR = 1.58, 95% CI 1.34–1.87), sick leave because of a musculoskeletal disorder (RR = 6.90, 95% CI 5.67–8.41), and sick leave because of trauma (RR = 3.42, 95% CI 2.59–4.50) were increased among blue-collar women. Employees of the local governments in the Finnish towns of Raisio, Valkeakoski, and Nokia. High-Income (Vahtera et al., 1999)
Cross-Sectional Occupational class Reduced activity days, time off work Blue-collar women vs. other womenC As compared with professional women, the odds of having a long-term condition (OR = 0.80, 95% CI 0.62–1.04), a short-term condition (OR = 0.89, 95% CI 0.62–1.04), reduced activity days (OR = 0.87, 95% CI 0.66–1.15), and time off work (OR = 0.95, 95% CI 0.66–1.39) were decreased among blue-collar women. Campbell National Health Monitor High-Income (Korda et al., 2002)
Cross-Sectional Occupational class Work ability Blue-collar women vs. other women As compared with white-collar women, blue-collar women had increased odds of lowered work ability (OR = 1.11, 95% CI 0.67–1.84). As compared with upper white-collar workers, odds of lowered work ability were also increased among blue-collar women (OR = 2.11, 95% CI 1.06–4.21). Helsinki Health Study High-Income (Aittomäki et al., 2003)
Longitudinal Role clarity, fairness, organizational climate Short sickness absence; long sickness absence; very long sickness absence Exposure-outcome among blue-collar women Among blue-collar women, the rate of short absence spells for those exposed to poor organizational climate were 1.6 times the rate of short absence spells for those with a favorable organizational climate (HR = 1.6, 95% CI 1.0–2.5). All employees of a forest industry corporation in Finland High-Income (Väänänen et al., 2004)
Longitudinal Race/ethnicity, age, education, occupation, shift worked, tenure, hourly rate of pay, full-time employment, having 2 jobs Lost-Worktime Injury or Illness Blue-collar women vs. other womenC Among women, the odds of reporting a lost-worktime injury or illness were decreased among machine operators (OR = 0.73, 95% CI 0.38–1.41) and craftswomen (OR = 0.57 (0.24, 1.33) as compared with laborers. The rates of lost-worktime injury or illness were increased among machine operators (IRR = 2.41, 95% CI 0.86–6.74) and decreased among craftswomen (IRR = 0.58, 95% CI 0.19–1.78) as compared with laborers. National Longitudinal Survey of Youth 1979 (NLSY) High-Income (Strong and Zimmerman, 2005)
Longitudinal Occupational class Sickness absence Blue-collar women vs. other women As compared with female executive managers and academics, the rates of long-term sickness absence were increased among skilled blue-collar women (RR = 2.06, 95% CI 0.69–6.17) and among semiskilled and unskilled women (RR = 2.76, 95% CI 1.00–7.65). Danish Work Environment Cohort Study (DWECS) High-Income (Christensen et al., 2008)
Cross-Sectional Occupational class Long sickness absence Blue-collar women vs. other women Odds of long-term sickness absence (OR = 2.45, 1.90–3.15) and work injury (OR = 5.63, 3.13-10.16) were higher among blue-collar women as compared to professionals/managers. SUMER Study High-Income (Niedhammer et al., 2008)
Longitudinal Family type, domestic responsibilities, negative work-family spillover Sickness absence Exposure-outcome among blue-collar women Among blue-collar women, the rates of sickness were increased for those with no children (RR = 1.11, 95% CI 0.96–1.30) and those with children 0–6 years old (RR = 1.11 (0.955–1.3) as compared to those with children 7–18 years old. Rates of sickness absence were increased if domestic responsibilities were the woman's duty alone (RR = 1.08, 0.93-12.4) or shared equally (RR = 1.15, 1.00–1.33) as compared to when they were somebody else's. Rates of sickness absence were increased for high negative work-family spillover (RR = 1.44, 1.25–1.66) and moderate work-family spillover (RR = 1.10, 0.95-1.27) as compared to low work-family spillover. Finnish 10-Town Study High-Income (Väänänen et al., 2008)
Longitudinal Work ability in midlife Disability Blue-collar women vs. other women As compared with white-collar women in excellent health, the odds of disability were increased among blue-collar women with excellent health (OR = 1.40, 95% CI 0.96–2.05), moderate health (OR = 2.41, 95% CI 1.78–3.26), and poor health (OR = 3.91, 95% CI 2.68–5.70). Finnish Longitudinal Study on Municipal Employees (FLAME) High-Income (von Bonsdorff et al., 2011)
Cross-Sectional Heart rate reserve Reduced work ability Exposure-outcome among blue-collar women Among blue-collar women, reduced work ability was inversely associated with increased heart rate reserve (OR = 0.30, 95% CI 0.04–2.30). New method for Objective Measurements of physical Activity in Daily living (NOMAD) study High-Income (Gupta et al., 2014)
Longitudinal Job demand, job control, social support, job insecurity, organizational injustice, lack of reward, discomfort in occupational climate Absence due to Accident Exposure-outcome among blue-collar women Among blue-collar women, the odds of absence due to accidents were increased for women with insufficient job control (OR = 1.95, 95% CI 0.63–6.11), high job insecurity (OR = 1.55, 95% CI 0.48–5.10), high organizational injustice (OR = 1.79, 95% CI 0.54–5.87), lack of reward (OR = 1.54, 95% CI 0.48–4.95), and discomfort in occupational climate (OR = 1.79, 95% CI 0.54–5.87). The odds of absence due to accidents among blue-collar women were decreased among those with high job demand (OR = 0.41, 95% CI 0.12–1.39) and high levels of inadequate social support (OR = 0.81, 95% CI 0.22–2.92). Workers at 23 manufacturing companies in the Incheon area of South Korea registered for health examinations with the department of occupational and environmental medicine at a university hospital. High-Income (Heo et al., 2015)
Health Behaviors Cross-Sectional Occupational class, hours worked per week, living arrangement, smoking status, MI, self-reported health Insufficient physical activity for health Blue-collar women vs. other women The odds of insufficient physical activity for health among blue-collar women were 1.55 times the odds of insufficient physical activity for health among professional women (OR = 1.55, 95% CI 1.4–1.8). Australian National Health Survey (ANHS) High-Income (Burton and Turrell, 2000)
Cross-Sectional Physical requirement, stress level, age, education, gender, race (white or black), drinking, smoking, self-reported health, workhours, any children younger than 18, have spouse, spouse working, spouse exercise level Vigorous Physical Activity Male vs. female blue-collar workersB Male blue collar workers were more likely to engage in light physical activity than female blue-collar workers (ß = 0.03, p > 0.05). Male blue-collar workers were significantly more likely to engage in vigorous physical activity as compared with female blue-collar workers (ß = 0.11, p < 0.001). Health and Retirement Study (HRS) High-Income (Wu and Porell, 2000)
Cross-Sectional Age, education, income, married, occupation, current smoker, commuting physical activity Leisure-time physical activity Blue-collar women vs. other womenC Blue-collar women were less likely to engage in leisure-time physical activity (OR = 0.52, 95% CI = 0.38–0.73), engage in commuting time physical activity (OR = 0.93, 95% CI 0.69–1.26) as compared with blue-collar women. Residents of Tianjin, China randomly selected using local population registers for 14 randomly selected communities. Upper-Middle-Income (Gang et al., 2002)
Cross-Sectional Age, Education, Occupation Daily energy expenditure (DEE); weekly physical activity (WPA); leisure-time physical activity (LTPA) Blue-collar women vs. other women Among female laborers and machine operators, daily energy expenditures were lower and weekly physical activity was higher as compared with female managers and professionals. Leisure time physical activity was similar in female laborers, machine operators, and managers. Workers at nine companies or factories located in the east (Kanto) and central (Chubu) areas of Japan. High-Income (Takao et al., 2003)
Cross-Sectional Age, education, occupational class, geographical location, BMI, workplace activity, physical activity level Achieving 10,000 Steps per Day Blue-collar women vs. other womenC Odds of reaching 10,000 steps among blue-collar women are 0.81 times the odds among female managers and professionals (OR = 0.81, 95% CI 0.21–3.06). Adults in Western Australia randomly selected from the White Pages telephone directory using proportional sampling. High-Income (McCormack et al., 2006)
Longitudinal Age, education, occupational class, smoking status, leisure-time physical activity, BMI Heterocyclic Amine (HCA) Intake Blue-collar women vs. other womenC As compared to blue-collar women, the likelihood of falling in the highest quintile of HCA intake was lower among medium-status white-collar women (OR = 0.81, 95% CI 0.69-0.96), high-status white-collar women (OR = 0.76, 95% CI 0.59-0.97) and among self-employed women (OR = 0.97, 95% CI 0.78–1.21). No differences were found between low-status white-collar women and blue-collar women (OR = 1.00, 95% CI 0.89–1.14). The Malmö Diet and Cancer (MDC) Study High-Income (Ericson et al., 2007)
Cross-Sectional Occupational class, hours at job, hours on unpaid care, dependents (<18 years) in the home, smoking status, weight status, perceived physical health Physical activity Blue-collar women vs. other women The odds of leisure-time physical activity among blue-collar women were 2.25 times the odds of leisure-time physical activity among women in professional jobs (OR = 2.25, 95% CI 0.92–5.50). 2000 Kings County Genuine Progress Indicators Survey High-Income (Kuiack et al., 2007)
Longitudinal Sex, education, native language, eating at work to cope with stress, intention to change fruit and vegetable intake, smoking status Change in fruit and vegetable consumption Exposure-outcome among blue-collar women Female construction workers participating in the Tools for Health cancer prevention intervention decreased their fruit and vegetable consumption (ß = -0.36, 95% CI - 2.18 to1.46) over the course of follow-up. Tools for Health (TFH) High-Income (Harley et al., 2010)
Cross-Sectional Occupational Class Leisure-time physical inactivity Blue-collar women vs. other women Blue-collar women were more likely be inactive during leisure time as compared with white-collar women (OR = 1.50, 95% CI 1.12–2.00). Finnish Health 2000 Survey High-Income (Mäkinen et al., 2010)
Longitudinal Age, education, employment, occupational class, live births, smoking status, alcohol intake, extra foods, self-rated health, SF12 physical and mental health measures Pedometer- and self-reported physical activity Blue-collar women vs. other womenC Blue-collar women engaged in 2.85 more minutes per week of physical activity as compared with white-collar women (ß = 2.85, 95% CI = - 0.10 - 5.81). Childhood Determinants of Adult Health (CDAH) Study High-Income (Cleland et al., 2011)
Cross-Sectional Occupational class, education, poverty-income ratio Physical inactivity Blue-collar women vs. other women The odds of physical inactivity were decreased among blue-collar women as compared with white-collar women (OR = 0.64, 95% CI 0.57–0.72). Third Korean National Health and Nutrition Examination Survey (KNHANES III) High-Income (Cho and Lee, 2012)
Cross-Sectional Education, household income, occupational class Healthy takeaway food; less healthy takeaway food Blue-collar women vs. other women Blue-collar women were less likely to eat healthy takeaway food (ß = 0.73, SE = 2.02) and more likely to eat less healthy takeaway food (ß = 3.67 (1.49) as compared with female managers and professionals. Adults randomly selected from the electoral roll of the Brisbane statistical subdivision in Australia. High-Income (Miura and Turrell, 2014)
Cross-Sectional Age, education, occupational class, marital status, smoking status, regular exercise, BMI, energy intake, alcohol intake Inadequate fruit and vegetable consumption (<5 servings per day) Blue-collar women vs. other womenC The odds of inadequate fruit and vegetable intake among female blue-collar women were 1.56 times the odds of inadequate fruit and vegetable intake among white-collar women (OR = 1.56, 95% CI 1.20–2.02). The EPIPorto Study High-Income (Oliveira et al., 2014)
Longitudinal BMI, country of birth, area of residence, educational qualification, marital status, number of children, occupational class, hours worked per week, being active, smoking status, alcohol consumption, being somewhat stressed Week-day sitting, Weekend-day sitting Blue-collar women vs. other womenC Blue-collar women engaged in less weekday sitting as compared with professional women (ß = - 1.30, 95% CI - 1.48, to 1.13). Weekend-day sitting was comparable among blue-collar women and professional women (ß = 0.06, 95% CI - 0.21, 0.10). Australian Longitudinal Study on Women's Health (ALSWH) High-Income (Uijtdewilligen et al., 2014)
Cross-Sectional Actual CVD risk, age, gender, education, knowledge of CVD risk, perceived general health, family function, social support, decision latitude, exposure to chemicals/noise, shift work Health-promoting behaviors Male vs. female blue-collar workersC Health risk scores among blue-collar women were increased as compared with blue-collar men (ß = 0.116, p = 0.120). Blue-collar workers at small companies recruited from occupational health centers or worksites during annual health checkups in South Korea. High-Income (Hwang et al., 2015)
Longitudinal BMI, country of birth, area of residence, educational qualification, marital status, number of children, occupational class, hours worked per week, smoking status, alcohol status Physically Active Blue-collar women vs. other womenC Odds of being physically active among blue-collar women were 0.94 times the odds of being physically active among professional women (OR = 0.94, 95% CI 0.83–1.05). Australian Longitudinal Study on Women's Health (ALSWH) High-Income (Uijtdewilligen et al., 2015)
Mental Health Cross-Sectional Job demands, job characteristics, physical environment, work-related social support, marriage, children, age, race, education Distress, happiness Exposure-outcome among blue-collar women Distress was inversely associated with substantive complexity of the job (ß = -0.048, p > 0.05); autonomy (ß = -0.013, p > 0.05); income (ß = -0.008, p > 0.05); satisfaction with co-workers (ß = -0.132, p < 0.01); company programs (ß = -.101, p > 0.05); being married (ß = -0.059, p > 0.05); age (p = -0.008, p > 0.05); race (ß = -0.108, p < 0.01); and education (ß = -0.002, p > 0.05). There was no association between distress and overtime or span of control. Employees at plants in seven manufacturing industries in south central Indiana High-Income (Loscocco and Spitze, 1990)
Cross-Sectional Age, current smoker, moderate/heavy alcohol, sever obesity, chronic disease, clean room, current chemical exposure Depression Exposure-outcome among blue-collar women The odds of depression among blue-collar with current chemical exposure were 1.46 times the odds of depression among blue-collar women without current chemical exposure (OR = 1.46, p > 0.05). There was no association between working in the clean room and depression among blue-collar women (OR = 1.00, p > 0.05 Female workers at a semi-conductor manufacturing plant in Pennsylvania represented by the International Brotherhood of Electrical Workers (IBEW). High-Income (Parkinson et al., 1990)
Cross-Sectional Age, education, marriage, income, smoking, obesity, major illness, life events, solvent exposure, job demands, job conflict, co-worker support, supervisor support, friend/relative support Depression Exposure-outcome among blue-collar women The odds of depression were increased among blue-collar women who smoked (ß = 0.21, p > 0.05); had a major illness (ß = 0.46, p < 0.05); had a life event (ß = 0.44, p < 0.001); were exposed to solvents (ß = 0.27, p < 0.05); or experienced increased job demands (ß = 0.19, p > 0.05) or job conflict (ß = 0.62, p < 0.001). Odds of depression were decreased among blue-collar women who were obese (ß = - 0.31, p > 0.05); and among those with support from co-workers (ß = -0.35, p < 0.05), supervisors (ß = -0.02, p > 0.05), or friends and relatives (ß = -0.50, p < 0.01). Female workers at a semi-conductor manufacturing plant in Pennsylvania represented by the International Brotherhood of Electrical Workers (IBEW). High-Income (Bromet et al., 1992)
Cross-Sectional Occupation, months on the job, decision latitude, social supports at work, work dissatisfaction, does not have enough money, economic tensions, family tensions, negative self-image, perceived health status, age, education, number of children < 15 years, number of utilities Depression, nervousness, sense of control Blue-collar women vs. other women The odds of depression were increased among garment workers as compared with service workers (ß = 0.77, 95% CI - 0.93 to 2.46) and decreased among electronic workers as compared with service workers (ß = -0.88, 95% CI - 2.57, 0.81). Women in Tijuana working as electronic maquiladora workers; garment maquiladora workers; service workers; and those with no labor force history Upper-Middle-Income (Guendelman and Silberg, 1993)
Cross-Sectional Age, sex, education, ethnic origin, subjective monotony Psychological Distress Exposure-outcome among blue-collar women Among blue-collar women, psychological distress is associated with subjective monotony (ß = 0.49, p < 0.005). Cardiovascular Occupational Risk Factors Determination in Israel Study (CORDIS) High-Income (Melamed et al., 1995)
Cross-Sectional Monotony, lack of control, self-esteem Psychological Distress Exposure-outcome among blue-collar women Among blue-collar women ≤ 35 years, psychological distress was positively associated with monotony (ß = 0.23, p > 0.05); lack of control (ß = 0.009, p > 0.05) and self-esteem (ß = 0.066, p > 0.05). Among blue-collar women > 35 years old, psychological distress was positively associated with monotony (ß = 0.050, p < 0.05) and self-esteem (ß = 0.099, p < 0.05), but inversely associated with lack of control (ß = -0.003, p > 0.05). Blue-collar workers at 37 factories of an industrial company in Finland High-Income (Kivimäki and Kalimo, 1996)
Cross-Sectional Responsibility for the safety of others, skill underutilization, sexual harassment and discrimination, overcompensation at work Psychological symptoms Exposure-outcome among blue-collar women Among female construction workers, having responsibility for the safety of others (ß = 0.456, p < 0.001), skill underutilization (ß = 0.399, p < 0.001), experiencing sexual harassment and discrimination on the job (ß = 0.258, p < 0.001), and having to overcompensate at work (ß = 0.254, p < 0.001). Female members of the Laborers' International Union of North America (LIUNA) in Seattle, Washington and Portland, Oregon. High-Income (Goldenhar et al., 1998)
Longitudinal Gender, workload score Psychosomatic complaints, intake of stress-related drugs Male vs. female blue-collar workers Intake of stress-related drugs was less frequent among female bus-drivers as compared with male bus drivers (ß = - 0.05, p > 0.05) and there was a negative interaction between gender and workload score (ß = - 0.03, p > 0.05). Psychosomatic complaints were less frequent among female bus-drivers as compared with male bus-drivers (ß = - 0.08, p > 0.05), and there was a positive interaction between gender and workload (ß = 0.18, p > 0.05). Full-time, employed urban bus drivers working at the same terminal in central Stockholm, Sweden. High-Income (Rydstedt et al., 1998)
Cross-Sectional Age, marital status, foreign background, education, occupational class, current financial support, weekly working hours, financial strain, work stress, drinking, smoking, psychoactive medication, somatic medication, cardiovascular disease, gastro-intestinal diseases, other diseases, pain, depression Burnout Blue-collar women vs. other womenC Odds of burnout among blue-collar women were 0.795 times the odds of burnout among women in other occupations (OR = 0.795, 95% CI 0.324–1.95). Adult women randomly selected from the general population resident in Stockholm County, Sweden. High-Income (Soares et al., 2007)
Case-Control Occupational class and labor market status; gross income; marital status; parenthood; place of residence; ethnicity Suicide Blue-collar women vs. other women Odds of suicide were among skilled blue-collar women were 1.1 times the odds of suicide among salaried female employees (OR = 1.1, 95% 0.8–1.5) and the odds of suicide among unskilled blue-collar women were 0.9 times the odds of suicide among salaried female employees (OR = 0.9, 95% CI 0.8–1.0). Incident cases of suicide and population controls drawn from a 5% random subsample of the total Danish population in the IDA database. High-Income (Andrés et al., 2010)
Longitudinal Distance from site; saw people dead or injured; participation in rescue operations; physical injuries; history of depression; harm to close friend or family member (injured or died); job relocation or temporary layoff; sick-leave; lives alone; occupational class Psychological distress Blue-collar women vs. other womenA The odds of psychological distress among female workers at the Toulouse industrial AZF disaster were 1.24 times the odds of psychological distress among female managers and professionals (OR = 1.24, 95% CI 0.48–3.41). Workers in the metropolitan area of Toulouse, France High-Income (Cohidon et al., 2009)
Cross-Sectional Housework, leisure active transport, biking to/from work, walking to/from work, sports Stress, distress Exposure-outcome among blue-collar women The risks of stress (RR = 2.661, 95% CI 1.098 - 6.447) and distress (RR = 2.911, 95% CI 1.055–8.031) were increased among blue-collar women engaging housework versus those not not. The risks of stress (RR = 1.114, 95% CI 0.478–2.597) and distress (RR = 1.008, 95% CI 0.478–2.344) were increased among blue-collar women engaging in leisure active transport versus those not. The risk of stress was decreased (RR = 0.714, 95% CI 0.266–1.918) and the risk of distress was increased (RR = 2.366, 95% CI 0.863–6.487) among blue-collar women walking to and from work versus those not. The risks of stress (RR = 1.564, 95% CI 0.607–4.030) and distress (RR = 2.202, 95% CI 0.793–6.115) were increased among blue-collar women participating in sports versus those not. Adults randomly selected from 46 randomly chosen Flemish municipalities. High-Income (Asztalos et al., 2009)
Cross-Sectional Work hours per day, overtime, salary; exposure to chemicals and toxic vapors/substances, exposure to vibration and dangerous equipment, high temperatures, physical dangers/unhealthy conditions at work, poor air/ventilation, crowded workstations and uncomfortable working postures, having a safe work environment, adequate protective clothing and equipment, adequate work-related welfare facilities; psychological job demands/workload, work is interesting, company informs about its achievements, on-site training courses, resources/help and equipment availability, supervisor-related, discrimination - intimidation or threats; household income inadequacy, social and family working hours fit, adequate sanitary living conditiolns/potable water, children under 18. Mental distress Exposure-outcome among blue-collar women Multiple associations reported between various measures of working hours and salary; safety and health; tasks and organizational aspects; extra-organizational factors and mental distress among blue-collar women. Blue-collar workers from 12 manufacturing companies in Lima, Perú. Upper-Middle-Income (Brunette et al., 2011)
Cross-Sectional Marital status, occupational category, household income, alcohol use, functional limitations, level of stress, experience of depressed feelings Suicidal Ideation Blue-collar women vs. other womenC Suicidal ideation was more prevalent among middle-aged women engaged in manual labor (OR = 2.77, 95% CI 1.20–6.42) as compared with female non-manual workers. Seoul Citizens Health and Social Indicators Survey High-Income (Moon and Park, 2012)
Cross-Sectional Age, smoking, company's gender equality index, employment hours, occupational class, high engagement in domestic work, number of children < 18 years, work demands, work control, work support, constrained physical heavy work load, work-home imbalance Emotional exhaustion Blue-collar women vs. other women Multiple point estimates reported for the association between emotional exhaustion and exposure to engagement in domestic work and exposure to psychosocial and physical work factors and work-home imbalance. Employees at 9 companies in computer science and 12 companies in the food industry in Sweden. High-Income (Ahlgren et al., 2012)
Cross-Sectional Psychological demand, social support, sex, marital status, age, education level, duration of employment, perceived work conditions, perceived work protection materials, work absenteeism Depression Male vs. female blue-collar workersC The odds of depression were increased among blue-collar women as compared with blue-collar men (OR = 2.1, 95% CI 0.7–5.2). Workers at the Le Lai II Shoe Manufacturing Factory in Haiphong City, Vietnam. Lower-Middle-Income (Minh, 2014)
Cross-Sectional Noise annoyance, sleeping time, education, occupational class, household income, smoking, alcohol drinking Depressive symptoms, suicidal ideation Blue-collar women vs. other womenA The odds of suicidal ideation were increased among blue-collar women as compared with white-collar women (OR = 1.27, 95% CI 0.96–1.67), and the odds of depressive symptoms were similar in blue- and white-collar women (OR = 1.02, 95% CI 0.76–1.37). Third Korean National Health and Nutrition Examination Survey (KNHANES III) High-Income (Yoon et al., 2014)
Mortality Longitudinal Occupational class, work control, work social support, psychological job demand, physical job demand Cardiovascular mortality Exposure-outcome among blue-collar women Among blue-collar women, odds of cardiovascular mortality were increased for those with high physical job demand (OR = 1.23, 95% CI 0.87–1.73) and decreased for those with high psychological demand (OR = 0.71, 95% CI 0.41–1.24). There was no association between work control (OR = 1.07, 95% CI 0.74–1.47) or work social support (OR = 1.04, 95% CI 0.74–1.47) and cardiovascular mortality among blue-collar women. Survey of Living Conditions High-Income (Hall et al., 1993)
Longitudinal Social class All-cause mortality, mortality from cardiovascular disease Blue-collar women vs. other women As compared with white-collar women, the risks of CHD mortality were higher among skilled (HR = 1.25, 95% CI 0.73–2.13) and unskilled (HR = 1.85, 95% CI 1.11–3.09) blue-collar women. The risks of all-cause mortality were higher for skilled (HR = 1.34, 95% CI 1.04–1.73) and unskilled (HR = 1.50, 95% CI 1.15–1.94) blue-collar women. The risks for all-cause mortality were increased among skilled (HR = 1.34, 95% CI 1.04–1.73) and unskilled (HR = 1.50, 95% CI 1.15–1.94). A random sample of the population of the eastern Finnish provinces of North Karelia and Kuopio. High-Income (Pekkanen et al., 1995)
Case-Control Occupational Class Deaths from Accidents, Violence, and Alcohol Blue-collar women vs. other women Alcohol-related deaths were more common in blue-collar women as compared with women in non-manual occupations (OR = 3.97, 2.86–5.52); deaths due to accidents and violence were more common in blue-collar women as compared with women in non-manual occupations (OR = 2.07, 95% CI 1.77–2.41). Incident cases of (i) alcohol-related deaths and (ii) deaths from accidents or violence in Moscow, Russia and controls with cancer-related causes of death. Upper-Middle-Income (Chenet et al., 1998)
Longitudinal Occupational Class All-cause mortality; mortality due to malignant neoplasms (various), diabetes mellitus, cerebrovascular disease, all heart disease, ischemic heart disease, nonmalignant respiratory disease, cirrhosis of the liver, and external causes Blue-collar women vs. other women Relative risks for all causes (0.98), all cancers (0.90), lung cancer (1.34), and breast cancer (0.96) were nonsignificant when mortality was compared to the US female population. No relationship between mortality and length of time employed in the industry or work area was identified. Workers from 13 high nickel alloys plants located throughout the United States. High-Income (Arena et al., 1999)
Longitudinal Age, occupational class, heart problems All-cause mortality Blue-collar women vs. other women The risk of mortality was increased among female workers as compared with female white-collar workers (RR = 1.16, p = 0.07). Level of Living Survey (LLS) High-Income (Kåreholt, 2001)
Longitudinal Occupational class Cardiovascular mortality Blue-collar women vs. other women The risk of observed mortality from cardiovascular disease was decreased among white-collar women as compared with blue-collar women (RR = .56, 95% CI 0.50–0.63). All residents of Halland, Sweden registered in the Population and housing censuses (FoB) High-Income (Baigi et al., 2002)
Longitudinal Education, housing, occupational class, gross income, family type, smoking Mortality due to respiratory disease Blue-collar women vs. other womenC As compared with blue-collar women, mortality from respiratory diseases was lower among white-collar women (HR = 0.68, 95% CI 0.47–0.99) and women outside of the workforce (HR = 0.94, 95% CI 0.63–1.39). The Copenhagen City Heart Study (CCHS) and the Glostrup Population Studies (GPS) High-Income (Prescott et al., 2003)
Longitudinal Shift work All-cause mortality Exposure-outcome among blue-collar women Among blue-collar women, the risk of all-cause mortality was decreased among those exposed to shift work as compared with those working day shifts (HR = 0.79, 0.50–1.26). National Survey of Living Conditions (ULF) High-Income (Åkerstedt et al., 2004)
Longitudinal Occupational class All-cause mortality Blue-collar women vs. other women As compared with high and middle bourgeoisie women, the risk of all-cause mortality was increased among unskilled blue-collar women (RR = 1.14, 95% CI 1.08–1.21) and skilled blue-collar women (RR = 1.06, 95% CI 1.00–1.12). Turin Longitudinal Study High-Income (Mamo et al., 2005)
Cross-Sectional Occupational class All-cause cancer mortality; lung cancer mortality Blue-collar women vs. other women There was no difference in cancer mortality risk among blue-collar women as compared with professional women (RR = 1.03, 95% CI 0.96–1.11). As compared with professional women, the risk of cancer mortality was increased among blue-collar women when breast cancer was excluded (RR = 1.12, 95% CI 1.02–1.22) and the risk of lung cancer mortality was increased among blue-collar women (RR = 1.12, 95% CI 1.02–1.22). The adult population of Australia nested within Statistical Local Areas (SLA) High-Income (Bentley et al., 2008)
Longitudinal Gender, race, age, exposure to chrysotile fibers Lung cancer mortality; Asbestosis Mortality; Pneumoconiosis and other respiratory diseases mortality Exposure-outcome among blue-collar women As compared with women whose cumulative exposure to chrysotile fibers was less than 1.5 fibre-years/ml, the risk of lung cancer mortality was decreased among those exposed to between 1.5 and 5 fibre-years/ml (RR = 0.59, 95% CI 0.22–1.61); but was increased among those exposed to between 5 and 15 fibre-years/ml (RR = 1.51, 95% CI 0.69–3.33), between 15 and 60 fibre-years/ml (0.69–3.33), between 60 and 120 fibre-years/ml (RR = 3.40, 95% CI 1.52–7.60), and more than 120 fibre-years/ml (RR = 3.84, 95% CI 1.41–10.5). Workers exposed to chrysotile in a South Carolina asbestos textile plant. High-Income (Hein et al., 2007)
Longitudinal Years smoked, drinks per week, age, male gender, race/ethnicity All-cause mortality Male vs. female blue-collar workersC All-cause mortality risk was increased among male urban transit operators as compared with female urban transit operators (HR = 1.464, 95% CI 0.767–2.795). San Francisco MUNI Health and Safety Study High-Income (Lipton et al., 2008)
Longitudinal Age of retirement for old-age pensioners, age of retirement for reduced earning capacity pensioners, year, age, family status, occupational class All-cause mortality Blue-collar women vs. other womenA As compared with blue-collar women, the all-cause mortality rate is lower among white-collar women (HR = 0.83, 95% CI 0.73–0.93). All insured embers of the Gmünder Ersatzkasse (GEK) compulsory health insurance fund who retired between the ages of 50 and 65 High-Income (Brockmann et al., 2009)
Longitudinal Work ability in midlife All-cause mortality Blue-collar women vs. other women As compared with white-collar women in excellent health, the rate of all-cause mortality was increased among blue-collar women with excellent health (HR = 1.30, 95% CI 0.97–1.74), moderate health (HR = 1.15, 95% CI 0.89–1.46), and poor health (HR = 1.44, 95% CI 1.10–1.89). Finnish Longitudinal Study on Municipal Employees (FLAME) High-Income (von Bonsdorff et al., 2011)
Longitudinal Area-remoteness index of Australia, index of relative socio-economic disadvantage (IRSD), time (years after diagnosis) and time squared, age, indigenous status, occupational class, marital status, cancer stage Breast Cancer Mortality Blue-collar women vs. other womenC The odds of breast cancer mortality were increased among blue-collar women as compared with professional women (OR = 1.27, 95% CI 1.08–1.51). Queensland Cancer Registry (QCR) High-Income (Dasgupta et al., 2012)
Longitudinal Job strain All-cause mortality Exposure-outcome among blue-collar women Among blue-collar women the odds of all-cause mortality were decreased for active work versus low strain work (OR = 0.77, 95% 0.56–1.07) and increased for passive work versus low strain work (OR = 1.17, 95% CI 0.89–1.55). There was no difference in the odds of all-cause mortality among blue-collar women engaged in high- versus low-strain work (OR = 0.97, 95% CI 0.73–1.29). Finnish Longitudinal Study on Municipal Employees (FLAME) High-Income (von Bonsdorff et al., 2012)
Longitudinal Occupational class, occupational position (manager vs. non-manager) All-cause mortality; CVD mortality; Cancer mortality Blue-collar women vs. other women The rates of all-cause mortality among blue-collar women were decreased as compared with white collar women (HR = 0.73, 95% CI 0.43–1.25), the rates of cardiovascular mortality were decreased among blue-collar women as compared with white-collar women (HR = 0.78, 95% CI 0.22–2.81) and the rates of cancer mortality were decreased among blue-collar women as compared with white-collar women (HR = 0.76, 95% CI 0.37–1.56). The Jichi Medical School Cohort Study (JMS) High-Income (Hirokawa et al., 2013)
Longitudinal Age; marital status; occupational class; SOC (comprehensibility, manageability, meaningfulness); psychiatric diagnoses (organic disorder, psychotic, dementia, alcohol use disorder) All-cause mortality Blue-collar women vs. other womenA The rate of all-cause mortality among blue-collar women was 1.63 times the rate of all-cause mortality among white-collar women (HR = 1.63, 95% CI 1.06–2.52). The Lundby Study Cohort High-Income (Mattisson et al., 2014)
Longitudinal Metalworking fluid Ischaemic Heart Disease Mortality Exposure-outcome among blue-collar women Among white female auto-workers, the risk of ischemic heart disease mortality was increased among those with cumulative exposure to soluble metalworking fluid of more than 3.44 mg/m3-year (HR = 1.89, 95% CI 0.74–4.86); between 1.81 and 3.44 mg/m3-year (HR = 2.44, 95% CI 0.96–6.22); between 0.77–1.80 mg/m3-year (HR = 2.40, 95% CI 0.97–5.9); and between 0–0.76 mg/m3-year (HR = 1.84, 95% CI 0.76–4.50) as compared with white female autoworkers with no exposure to soluble metalworking fluids. Risk was increased among those with cumulative exposure to synthetic metalworking fluid greater than 0.65 mg/m3-year (HR = 1.37, 95% CI 0.82–2.29) and between 0–0.65 mg/m3-year (HR = 1.29, 95% CI 0.80–2.07) as compared with white female autoworkers with no exposure to synthetic metalworking fluids. Female hourly automobile production workers from three large manufacturing plants in Michigan High-Income (Costello et al., 2014)
Case-Control Occupational class, current smoking, African American Sudden Cardiac Death Blue-collar women vs. other women Odds of sudden cardiac among white-collar women were 1.49 times the odds of sudden cardiac death among blue-collar women (OR = 1.49, 95% CI 0.81–2.75). Oregon Sudden Unexpected Death Study High-Income (Zhang et al., 2015)
Musculoskeletal Longitudinal Physical Workload Occupation and osteoarthrosis of the hip and knee Exposure-outcome among blue-collar women Among blue-collar women born in 1905–1924, the risks were increased for hospitalization due to osteoarthrosis of the hip (RR = 1.6, 95% CI 0.9–3.1) and knee (RR = 1.4, 95% CI 0.6–3.2) among those with high versus low exposure occupations. Among blue-collar women born in 1925–1945, the risk was increased for hospitalization for osteoarthrosis of the hip (RR = 1.1, 95% CI 0.9–1.5) and knee (RR = 1.9, 95% CI 1.3–2.9) among those with high versus low exposure occupations. Residents of one of 13 Swedish counties who reported the same occupation in the 1960 and 1970 census. High-Income (Vingård et al., 1991)
Cross-Sectional Previous pain symptoms, muscle tension, age, psychological problems, working hours, family relationship Pain symptoms of the head, neck, shoulders/upper arms, lower arms, low back, hip, thighs, knee and ankles Exposure-outcome among blue-collar women The main individual risk factor identified was experience of previous similar symptoms in the same body region. Other individual factors were signs of psychological problems and tendency of muscle tension. Female production workers employed by a single Norwegian clothing manufacturing companies. High-Income (Westgaard and Jansen, 1992)
Cross-Sectional Positive affectivity, negative affectivity, age, sex, education, tenure, alcohol involvement, autonomy, routinization, job hazards, role ambiguity, role conflict, work overload, supervisory support, co-worker support Occupational injury Male vs. female blue-collar workersB Blue-collar men were less likely to experience occupational injury than blue-collar women (ß = -0.29, p < 0.05). Blue-collar unionized employees at a single manufacturing plant in Victoria, Australia High-Income (Iverson and Erwin, 1997)
Longitudinal High mental load at work, monotonous work, overtime work, dissatisfactory leisure time, high mental load at work + dissatisfactory leisure time Disorders of the neck; disorders of the shoulder Exposure-outcome among blue-collar women Among blue-collar women, reported medical treatment or consultation for disorders of the shoulder were associated with high mental load at work (PR = 1.2, 95% CI 0.3–4.4), overtime work, (PR = 2.7, 95% CI 1.1–6.9), and high mental load at work with unsatisfactory leisure time (PR = 1.7, 95% CI 0.6–4.8) relative to potential risk factors in 1969. Unsatisfactory leisure time was associated with decreased reported medical treatment or consultation for disorders of the shoulder (PR = 0.7, 95% CI 0.3–1.7). The REBUS Study High-Income (Fredriksson et al., 1999)
Longitudinal Duration of exposure, age, smoking, BMI, living alone with children, job strain, social support, stress Shoulder Disorders Exposure-outcome among blue-collar women Among female sewing machine operators, the risk of developing a shoulder disorder for those wit high versus low shoulder support (RR = 3.72, 95% CI 1.22–11.30), increased neck-shoulder pain scores (RR = 1.02, 95% CI 1.00–1.05), smoking (RR = 3.93, 95% CI 1.33–11.58), and living alone with children (RR 3.58, 95% CI 0.87–14.68). Danish Project on Research and Intervention in Monotonous Work (PRIM Study) High-Income (Kaergaard and Andersen, 2000)
Longitudinal Job demand, job control, supervisor support, coworker support, age, work duration Work injury Exposure-outcome among blue-collar women The odds of work injury among blue-collar were increased among those with high job demand (OR = 1.30, SE = 0.08331). Odds of work injury were decreased among those with high job control (OR = 0.94, SE = 0.03650) and high coworker support (OR = 0.71, SE = 0.18003). There was no association between work injury and supervisor support (OR = 1.02, SE = 0.12049), age (OR = 1.02, SE = 0.04177), and working duration (OR = 1.03, SE = 0.05987). Employees of a small aerosol products manufacturing plant in Japan High-Income (Murata et al., 2000)
Case-Control Stature; body weight; sitting with neck bent forward; arms, hands at/above shoulder height; decision latitude index, precision movements required; fixed working postures; uncomfortable work postures; civil status Neck and shoulder problems Exposure-outcome among blue-collar women Increased odds of neck and shoulder problems among blue-collar women were associated with the number of hours per day spent with the arms or hands at or above shoulder height (OR = 1.087, 95% CI 1.031–1.365), decision latitude index (OR = 1.175, 95% CI 1.040 1.327), required precision movement (OR = 1.714, 95% CI 0.589–4.989), fixed work postures (OR = 1.947, 95% CI 0.796–4.766), uncomfortable work postures (OR = 1.700, 95% CI 0.697–4.149), and partnership with children under 13 years (OR = 3.357, 95% CI 0.996–11.31), partnership with no children under 13 years (OR = 3.473, 95% CI 1.019–11.84), and being single with children under 13 years (OR = 4.278, 95% CI 0.823–22.25). Decreased odds of neck and shoulder problems were associated with increased stature (OR = 0.784, 95% CI 0.563–1.092). Body weight (OR = 0.991, 95% CI 0.817–1.202) and hours per day spent with arms or hands at or above shoulder height (OR = 1.039, 95% CI 0.879–1.229) were not associated with neck and shoulder problems. Women employed at one of 26 companies in the metal and food industries from three Swedish counties. High-Income (Björkstén et al., 2001)
Longitudinal Occupational class at age 30; parents' occupational class, school grade, smoking, and physical activity at age 16; being single at age 21; job control and physical working condition at age 30. Musculoskeletal Disorders Blue-collar women vs. other women The odds of a musculoskeletal disorder at age 21 among blue-collar women were 1.43 times the odds of a musculoskeletal disorder at age 21 among white-collar women (OR = 1.43, 95% CI 0.97–2.11). Follow-up of a baseline survey of 16 year old pupils in their last year of compulsory schooling in the industrial Northern Swedish town of Lulea. High-Income (Khatun et al., 2004)
Cross-Sectional Years of formal education, occupational class Hospitalization because of back disorders Blue-collar women vs. other women The risk of hospitalization because of back disorders were increased among blue-collar women age 25-34 (RR = 1.6, 95% CI 1.2–2.2), age 35-44 (RR = 1.4, 95% CI 1.2–1.6), age 45-54 (RR = 1.3, 95% CI 1.1–1.4), and age 55–64 (RR = 1.3, 95% CI 1.1–1.5) as compared to their white-collar counterparts. Population of Finland High-Income (Kaila-Kangas et al., 2006)
Cross-Sectional Gender, age, smoking, insomnia symptoms, job type, industrial sector, work experience Occupational injuries Blue-collar women vs. other womenC Odds of occupational injuries among female manufacturers were 4.26 times the odds of occupational injury among female managers and clerical workers (OR = 4.26, 95% CI 2.23-8.13). Workers of small-scale manufacturing factories (those with less than 50 workers) in Yashio city, Saitama prefecture, Japan. High-Income (Nakata et al., 2006)
Longitudinal Sex, age category, education, time since hire, time in current job, physical demand, race, plant type Traumatic Injury; OSHA Recordable Injuries Male vs. female blue-collar workersC The odds of traumatic injury among blue-collar women were increased as compared with blue-collar men (OR = 1.57, 95% CI 1.33–1.85). The odds of OSHA recordable injuries among blue-collar women were increased as compared with blue-collar men (OR = 1.72, 95% CI 1.34–2.20). The American Manufacturing Cohort Study (AMC) High-Income (Pollack et al., 2007)
Cross-Sectional Gender, age group, ethnicity, education level, marital status, living with children, supporting families outside of household, BMI, physical activity, smoking behavior, physician diagnosed systemic illness, years of employment in garment industry Neck, Shoulder, and Distal Upper Extremity Pain Male vs. female blue-collar workersC The odds of neck and shoulder pain were decreased among blue-collar men as compared with blue-collar women (OR = 0.5, 95% CI 0.28–0.90). The odds of distal upper extremity pain among blue-collar men were decreased as compared with blue-collar women (OR = 0.55, 95% CI 0.28–1.09). Sewing machine operators working at one of 13 garment shops in Los Angeles selected using convenience sampling. High-Income (Wang et al., 2007)
Cross-Sectional Occupational class Work Injury Blue-collar women vs. other women Odds of poor self-rated health were increased among blue-collar women as compared to female professionals managers (OR = 2.02, 95% CI 1.57–2.61). Among blue-collar women, odds of long-term sickness absence (OR = 2.45, 95% CI 1.90–3.15) and work injury (OR = 5.63, 95% CI 3.13-10.16) were also increased as compared to female managers and professionals. SUMER Study High-Income (Niedhammer et al., 2008)
Longitudinal Sex Acute Injuries; MSD-Related Injuries Male vs. female blue-collar workers The odds of acute injuries among female smelters were increased as compared with male smelters (OR = 1.201, 95% CI 1.151–1.295). The odds were also increased for total recordable acute injuries (OR = 1.158, 95% CI 1.012–1.326) lost-work-day acute injuries (OR = 1.097, 95% CI 0.512–2.348); MSD-related injuries (OR = 1.119, 95% CI 1.097–1.311); total recordable MSD-related injuries (OR = 1.334, 95% CI 1.174–1.515) and lost-work-day MSD-related injuries (OR = 1.285, 95% CI 0.783–2.109) among female smelters as compared with male smelters. The American Manufacturing Cohort Study (AMC) High-Income (Taiwo et al., 2008)
Longitudinal Occupational class Carpal tunnel syndrome Blue-collar women vs. other women The risk of carpal tunnel syndrome was increased among blue-collar women as compared with non-working person (RR = 3.0, 95% CI 2.5–3.6). Risk was increased among women in agriculture (RR = 2.5, 95% CI 2.0–3.2), construction (RR = 4.7, 95% CI 1.0–13.0) and manufacturing (RR = 2.1, 95% CI 1.7–2.5) as compared to non-working women. Adult residents of the Maine and Loire (M&L) region in west-central France. High-Income (Roquelaure et al., 2008)
Longitudinal Depressive symptoms Occupational injury Exposure-outcome among blue-collar women Among blue-collar women, risk of occupational injury was increased among those with depressive symptoms (RR = 2.04, 95% CI 1.41–2.95). Workers at 35 small- or medium-sized manufacturing companies in the Incheon area of South Korea in the Group Occupational Health Service at Inha University Hospital. High-Income (Kim et al., 2009)
Case-Control Occupational class, BMI, height, parity Surgically treated CTS Blue-collar women vs. other women The odds of surgically-treated carpal tunnel syndrome among blue-collar women were increased as compared with white-collar women (OR = 9.1, 95% CI 4.8–17.4). Cases of surgically-treated CTS and controls selected among blue- and white-collar workers, other workers, and housewives living in the Region of Tuscany, Italy. High-Income (Mattioli et al., 2009)
Longitudinal Occupational class, industrial sector Carpal tunnel syndrome Blue-collar women vs. other women The risk of carpal tunnel syndrome (CTS) among blue-collar women was increased as compared with female farmers (RR = 2.9, 95% CI 2.5–3.4). The risk of CTS among women in construction was increased as compared with women in manufacturing (RR = 2.0, 95% CI 1.7–2.4). Adult residents of the Maine and Loire (M&L) region in west-central France. High-Income (Roquelaure et al., 2009)
Cross-Sectional Pain severity, weaver's selection of remedial measures, weaver's perception to the cause of pain, age, working hours, marital status, job tenure, literacy, psychosocial variables Musculoskeletal Disorders Exposure-outcome among blue-collar women The odds of musculoskeletal disorders were increased among female handloom workers older than 25 (OR = 2.9, 95% CI 1.2–7.4), with more than 10 years of tenure (OR = 2.1, 95% CI 1.1–4.6), with mental overload (OR = 3.7, 95% CI 1.0–13.8) and among those who were literate (OR = 2.2, 95% CI 1.2–3.9). Weavers working at handloom or powerloom units in the Ahmedabad district of India. Lower-Middle-Income (Nag et al., 2010)
Cross-Sectional Work hours per day, overtime, salary; exposure to chemicals and toxic vapors/substances, exposure to vibration and dangerous equipment, high temperatures, physical dangers/unhealthy conditions at work, poor air/ventilation, crowded workstations and uncomfortable working postures, having a safe work environment, adequate protective clothing and equipment, adequate work-related welfare facilities; psychological job demands/workload, work is interesting, company informs about its achievements, on-site training courses, resources/help and equipment availability, supervisor-related, discrimination - intimidation or threats; household income inadequacy, social and family working hours fit, adequate sanitary living conditions/potable water, children under 18. Musculoskeletal Pain Exposure-outcome among blue-collar women Multiple associations reported between various measures of working hours and salary; safety and health; tasks and organizational aspects; extra-organizational factors, and musculoskeletal pain among blue-collar women. Blue-collar workers from 12 manufacturing companies in Lima, Perú. Upper-Middle-Income (Brunette et al., 2011)
Cross-Sectional Age, marital status, type of carpet-weaving loom, weaving style, stature, work hours, work experience Elbow pain; Forearm pain; Wrist pain Exposure-outcome among blue-collar women Among female carpet-weavers, type of carpet weaving loom (fixed vs. moving vertical) was significantly associated with shoulder pain (OR = 3.422, 95% CI 2.026-6.124); elbow pain (OR = 2.439, 95% CI 1.565-3.801); forearm pain (OR = 2.621, 95% CI 1.715-4.006); and wrist pain (OR = 2.299, 95% CI 1.539-3.433). Stratified random sample of carpet weavers in urban and rural regions within the Kerman, Esfahan, and East Azerbaijan provinces. Upper-Middle-Income (Motamedzade and Moghimbeigi, 2012)
Cross-Sectional Age, smoking, company's gender equality index, employment hours, occupational class, high engagement in domestic work, number of children < 18 years, work demands, work control, work support, constrained physical heavy work load, work-home imbalance Neck and shoulder disorders; low back disorders Blue-collar women vs. other womenA There was no evidence of a difference between blue- and white-collar women's risk for neck and shoulder disorders (OR = 1.0, 95% CI = 0.5–1.7) Employees at 9 companies in computer science and 12 companies in the food industry in Sweden. High-Income (Ahlgren et al., 2012)
Longitudinal Occupation Surgically treated osteoarthritis in the hip or knee Blue-collar women vs. other women Among female construction workers, the rate for surgically-treated osteoarthritis of the hip as compared with female office workers (HR = 1.21, 95% CI 1.03–1.43), and the rates of surgically-treated osteoarthritis of the knee were increased as compared with female office workers (HR = 1.37, 95% CI 1.14–1.64). Danish residents employed in one of five occupational groups: floor- and bricklayers, construction workers, farmers, healthcare assistants, and office workers. High-Income (Andersen et al., 2012)
Cross-Sectional Age, BMI, job type, education, time in industry, monthly income Musculoskeletal complaints of the back, knee, and upper limb Blue-collar women vs. other womenC As compared with female sewing machine operators, the odds were decreased for back complaints (OR = 0.62, 95% CI 0.28–1.39) and knee complaints (OR = 0.70, 95% CI 0.21–2.34), and increased for upper limb complaints (OR = 1.28, 95% CI 0.36–4.50) as compared with female quality control assistants. Female garment workers employed in factories in the Koggala FTZ in Sri Lanka. Lower-Middle-Income (Lombardo et al., 2012)
Longitudinal Gender, manager type, location, year, race/ethnicity, age when started in department, tenure when started in department, department is high demand First-aid injury, reportable injury Male vs. female blue-collar workers The rates for first acute injury were increased among blue-collar women as compared with blue-collar men (HR = 1.21, 95% CI 1.06, 1.39). The American Manufacturing Cohort Study (AMC) High-Income (Kubo et al., 2013)
Longitudinal Age, sex, time in the union, predominant work Hand and Finger Injuries Male vs. female blue-collar workersC Among those with or paid lost time, the rates of hand and finger injuries among blue-collar women is increased as compared with blue-collar men (RR = 1.2, 95% CI 1.0–1.5). Among those with paid lost time, the rates of hand and finger injuries is also increased among blue-collar women as compared with blue-collar men (RR = 1.8, 95% CI 1.1–3.0). Union carpenters working in Washington State. High-Income (Lipscomb et al., 2013)
Cross-Sectional Work experience, prolonged working hours, awkward posture, perceived high job demand Musculoskeletal Disorders Exposure-outcome among blue-collar women Among blue-collar women, the odds of musculoskeletal disorders were increased among those with five or more years of work experience (OR = 1.79, 95% CI 0.72–4.44), with prolonged working yours (OR = 7.63, 95% CI 2.06, 28.31), with awkward work postures (OR = 43.79, 95% CI 17.09–112.20), and perceived high job demand (OR = 1.16, 95% CI 0.34–3.98). Randomly sampled female workers using hand-operated rebar benders in a northeast province of Thailand. Upper-Middle-Income (Hanklang et al., 2014)
Longitudinal Gender Injury Male vs. female blue-collar workers The odds of injury among blue-collar women were increased as compared with blue-collar men (OR = 1.58, 95% CI 1.48–1.67). The American Manufacturing Cohort Study (AMC) High-Income (Tessier-Sherman et al., 2014)
Longitudinal Physical demand, exposure to heat, psychological demand, job control, race/ethnicity, job tenure, age, sex First aid injury and first aid MSD; Serious injury and serious MSD; First aid MSD only; Serious MSD only Male vs. female blue-collar workersB Among blue-collar women, the risk was increased for first aid injury (RR = 1.51, 95% CI 1.31–1.73), serious injury and serious musculoskeletal disorder (RR = 1.55, 95% CI 1.23–1.93), first aid musculoskeletal disorder only (RR = 1.26, 95% CI 1.00–1.59) and serious musculoskeletal disorder only (RR = 1.75, 95% CI 1.25–2.46) as compared with blue-collar men. The American Manufacturing Cohort Study (AMC) High-Income (Cantley et al., 2016)
Cross-Sectional Total sitting time per day Neck and shoulder pain intensity Exposure-outcome among blue-collar women Among blue-collar women, the odds of NSP were increased for high total sitting time vs. moderate total sitting time (OR = 1.19, 95% CI 0.31–4.15) and the decreased for low total sitting time vs. moderate total sitting time (OR = 0.80, 95% CI 0.21–2.99). The odds for NSP were increased for high work sitting vs. moderate work sitting (OR = 1.17, 95% CI 0.32–4.33) and the odds for NSP were the same low work sitting vs. moderate work sitting (OR = 1.01, 95% CI 0.28–3.59). The odds for NSP for were the same high leisure sitting vs. moderate work sitting (OR = 1.02, 95% CI 0.28–3.74) and were decreased for NSP for low leisure sitting vs. moderate leisure sitting (OR = 0.86, 95% CI 0.25–3.02). New method for Objective Measurements of physical Activity in Daily living (NOMAD) Study High-Income (Hallman et al., 2015)
Other Cross-Sectional Age, current smoker, moderate/heavy alcohol, sever obesity, chronic disease, clean room, current chemical exposure Memory loss, headaches, head exploding light-headed, room spinning, tremor, coordination, numbness, cold hands/feet, appetite change, funny taste, swallowing, palpitations, weakness/fatigue, rashes, chronic cough, abdominal pain Exposure-outcome among blue-collar women Among blue-collar women with current chemical exposure, the odds were increased for memory loss (OR = 1.23, p > 0.05), headaches (OR = `.40, p > 0.05), light-headedness (OR = 1.12, p > 0.05), room spinning (OR = 1.14, p > 0.05), tremor (OR = 1.07, p > 0.05), coordination (OR = 1.43, p > 0.05), numbness (OR = 1.16, p > 0.05), and cold hands and feet (OR = 1.42, p > 0.05) as compared to blue-collar women without current chemical exposure. Odds of symptoms of "head exploding" were decreased among those with current chemical exposure (OR = 0.93, p > 0.05). Workers at microelectronics plant represented by IBEW in Pennsylvania High-Income (Parkinson et al., 1990)
Cross-Sectional Age, education, marriage, income, smoking, obesity, major illness, life events, solvent exposure, job demands, job conflict, co-worker support, supervisor support, friend/relative support Headache, lightheaded, vertigo, weakness, memory loss, abdominal pain, rash, multiple symptoms Exposure-outcome among blue-collar women Among blue-collar women with current solvent exposure, the odds were increased for depression (ß = 0.19, p < 0.05), lightheadedness (ß = 0.29, p < 0.01), vertigo (ß = 0.39, p < 0.01), weakness (ß = 0.33, p < 0.005), memory loss ( ß = 0.33, p < 0.005), abdominal pain (ß = 0.54, p < 0.005), rash (ß = 0.20, p = 0.07), and multiple symptoms (ß = 0.23, p < 0.01). Workers at semiconductor plant represented by IBEW in the mid-Eastern US High-Income (Bromet et al., 1992)
Cross-Sectional Occupation Headaches that match cervical origin pain patterns Blue-collar women vs. other women As compared with blue-collar women, the odds of cervical origin headache were increased among female managers and professionals (OR = 2.94, 95% CI 1.3–6.6), and female clerical workers (OR = 1.37, 95% CI 0.6–3.2). Residents of two adjoining Tasmanian municipalities randomly sampled from electoral rolls. High-Income (Grimmer, 1993)
Cross-Sectional Age, education, sex, alcohol, solvent exposure index Continuous performance test; pattern comparison (latencies); pattern memory (latencies) Male vs. female blue-collar workersB Continuous performance test scores were decreased among blue-collar men as compared with blue-collar women (ß =–0.019, SE = 0.014), and pattern comparison (ß = 0.031, SE = 0.033) and pattern memory (ß = 0.020, SE = 0.029) were increased among blue-collar men as compared with blue-collar women. Workers in six paint manufacturing plants in northern Taiwan High-Income (Tsai et al., 1997)
Cross-Sectional Overcompensating at work, job certainty, sexual harassment and discrimination Insomnia, nausea, headaches Exposure-outcome among blue-collar women Among female construction workers, the odds of insomnia were increased among those who overcompensated at work (OR = 1.41, 95% CI 1.14–1.74) and decreased among those with job certainty (OR = 0.85, 95% CI 0.771–0.942). The odds of nausea were increased among those experiencing sexual harassment and discrimination (OR = 1.33, 95% CI 1.11–1.60). The odds of headache were increased among those experiencing sexual harassment and discrimination (OR = 1.21, 95% CI 1.02–1.43). Female members of the Laborers' International Union of North America (LIUNA) in Seattle, Washington and Portland, Oregon. High-Income (Goldenhar et al., 1998)
Cross-Sectional Age, area of factory Hearing impairment Exposure-outcome among blue-collar women Audiometric tests performed on 69 female workers from the weaving section revealed that workers with more than 10 years of noise exposure had the worst hearing threshold levels at 1,000 and 4,000 Hz. Female workers from the weaving section of a textile factory in Hanoi, Vietnam Lower-Middle-Income (Nguyen et al., 1998)
Longitudinal Gender, workload score Fatigue spillover Male vs. female blue-collar workers Fatigue spillover was less frequent among female bus-drivers as compared with male bus drivers (ß = - 0.05, p > 0.05) and there was a negative interaction between gender and workload score (ß = - 0.24, p > 0.05). Full-time, employed urban bus drivers working at the same terminal in central Stockholm, Sweden. High-Income (Rydstedt et al., 1998)
Cross-Sectional Magnetic field exposure, smoking, age, light at night, duration of sleep, depression, miscarriages Nocturnal 6-hydroxymelatonin sulfate excretion Blue-collar women vs. other women Normalized 6-OHMS secretion was decreased among blue-collar women exposed to magnetic fields (ß = -4.06, SE = 1.51) as compared to office workers not exposed to magnetic fields. Female workers from a garment factory in Kuopio, Finland and a reference group of employees of a governmental organization and university staff members. High-Income (Juutilainen et al., 2000)
Cross-Sectional Age, BMI, Illness, ergonomic stress level, environmental annoyance, perceived control Serum Uric Acid Exposure-outcome among blue-collar women Among blue-collar women, serum urine acid levels were negatively associated with ergonomic stress levels (ß = -0.18, SE = 0.10), environmental annoyance (ß = - 0.05, SE = 0.03), and perceived control (ß = - 0.03, SE = 0.02). Cardiovascular Occupational Risk Factors Determination in Israel Study (CORDIS) High-Income (Shirom et al., 2000)
Cross-Sectional Occupational class Long-term condition, short-term condition Blue-collar women vs. other women As compared with professional women, the odds of long-term conditions were decreased among blue-collar women (OR = 0.80, 95% CI 0.62–1.04) and the odds of short-term conditions were decreased among blue-collar women (OR = 0.89, 95% CI 0.64–1.24). Campbell National Health Monitor High-Income (Korda et al., 2002)
Cross-Sectional Noise level Tooth abrasion Exposure-outcome among blue-collar women Among blue-collar women, the odds of tooth abrasion were increased among those exposed to intense noise as compared with those in the preparation department (OR = 3.74, 95% CI 1.42–7.85). Workers at a large wool-producing company in Montenegro, Serbia. Upper-Middle-Income (Kovacevic and Belojevic, 2006)
Longitudinal Technical job, natural menopause, smoking > 10 cigarettes per day, technical and community education, education, 1-2 pregnancies, age category Blood Lead Levels, Calcaneus Bone Lead Content Blue-collar women vs. other women Blood lead levels among women in technical jobs (for example, miners) were higher than blood lead levels among women in administrative support or sales (ß = 0.242, 95% CI = -0.115 - 0.599). Bunker Hill Study High-Income (Potula and Kaye, 2006)
Cross-Sectional Age, gender, annual income level, marital status Fibromyalgia Male vs. female blue-collar workersC The odds of fibromyalgia were increased among blue-collar women as compared with blue-collar men (OR = 15.01, 95% CI 1.90–118.50). Female workers at four textile factories in Denizli, Turkey. Upper-Middle-Income (Cobankara et al., 2011)
Cross-Sectional Age, sex, race, marital status, education, pain, number of medical comorbidities, depressive symptoms, alcohol problems, smoking, physical activity obesity Sleep Quality Male vs. female blue-collar workersC Sleep score quality was decreased among blue-collar women as compared with blue-collar men (ß = - 0.100, p = 0.041). A convenience sample of operating engineers coming to either an apprentice certification or Hazardous Materials (Hazmat) refresher course in Michigan. High-Income (Choi et al., 2013)
Cross-Sectional Former rotating shift work, recent rotating shift work, persistent rotating shift work; academic educational level; married/cohabitating, child-rearing responsibility; hepatitis B carrier, metabolic syndrome, anemia High need for recovery after work Exposure-outcome among blue-collar women Among blue-collar women, work-related fatigue was decreased among those currently married or cohabiting (OR = 0.5, 95% CI 0.2–0.9), and increased among those with child-rearing responsibilities (OR = 1.9, 95% CI 1.0–3.7). Workers at a single semiconductor manufacturing company receiving compulsory periodic health checkups. High-Income (Lin et al., 2015)
Reproductive & Sexual Health Cross-Sectional Occupation, age, smoking, parity, years of education Birthweight of most recent birth Blue-collar women vs. other women As compared with women who were service workers during pregnancy, lower birth weight infants were born to women who were garment workers (ß = - 653, 95% CI - 1,041 to 265) or electronics workers (ß = - 337, 95% CI - 682 to 9). Women in Tijuana working as electronic maquiladora workers; garment maquiladora workers; service workers; and those with no labor force history Upper-Middle-Income (Eskenazi et al., 1993)
Cross-Sectional Occupation, education, place of residence Natural Menopause Blue-collar women vs. other women As compared with lower white-collar workers, the odds of natural menopause were increased among blue-collar factory workers (OR = 1.17, 95% CI 0.96–1.42). Adult women randomly selected form the Finnish Population Register High-Income (Luoto et al., 1994)
Cross-Sectional Race, age, occupational class, smoking, contraception, coitarche, partners last year, total partners, anal intercourse, sexual intercourse with partner from abroad, no non-regular partners, diagnosis with other STI, past history of STI, high vaginal swab HSV-2 Infection Blue-collar women vs. other women Odds of HSV-2 infection among blue-collar women were increased as compared with white-collar women (OR = 4.14, 95% CI 1.33–12.92). Women newly attending a genitourinary clinic in London High-Income (Evans et al., 2003)
Longitudinal Occupational class Prematurity, low birth weight, SGA, LGA, perinatal mortality, birth weight Blue-collar women vs. other women As compared with upper white-collar workers, the odds of prematurity were increased (OR = 1.14, 95% CI 1.07–1.22), the odds of low birth weight were increased (OR = 1.25, 95% CI 1.16–1.34), the odds of SGA were increased (OR = 1.44, 95% CI 1.31–1.58), the odds of LGA were increased (OR = 1.24, 95% CI 1.14–1.36), and the odds of perinatal mortality were increased (OR = 1.44, 95% CI 1.13–1.83) among blue-collar women between 2003 and 2006. Finnish Medical Birth Register High-Income (Gissler et al., 2009)
Longitudinal Rubber cohort membership Birth weight Blue-collar women vs. other women Among women who were rubber workers during pregnancy, the odds were increased for having a girl (OR = 1.15, 95% CI 1.02–1.13) and having a small-for-gestational age child (OR = 2.15, 95% CI 1.45–3.18) as compared with food workers. Female workers at 12 Swedish rubber production facilities and female members of the Food Worker's Union. High-Income (Jakobsson and Mikoczy, 2009)
Quasi-Experimental Parental leave Fertility Exposure-outcome among blue-collar women Among blue-collar women, the effect of the 1990 reform was an increase in the probability of having an additional birth in 0–36 months (ß = 0.048, SE = 0.016), in 0–120 months (ß = 0.036, SE = 0.016), and 17–28 months (ß = 0.078, SE = 0.013). The probability of having an additional birth was decreased in 0 - 16 months (ß = -0.031, SE = 0.009) and 29–120 months (ß = -0.008, SE = 0.016). Women giving birth to their first child recorded in the Austrian Social Security Database. High-Income (Lalive and Zweimüller, 2009)
Cross-Sectional Age, education, year of conception, employment area, medical conditions during pregnancy, smoked during pregnancy, drank alcohol during pregnancy Adverse Pregnancy Outcomes Blue-collar women vs. other women The odds of congenital anomalies among female laboratory workers in an aluminum smelter were increased during employment as compared with women who gave birth prior to employment (OR = 7.89, 95% CI 1.16–53.77). The American Manufacturing Cohort Study (AMC) High-Income (Sakr et al., 2010)
Cross-Sectional Age, marital status, education, age at first sexual exposure, sources of knowledge on HIV/AIDS Use of condoms; sex with multiple partners; drug abuse Exposure-outcome among blue-collar women Among female garment workers, increased knowledge score was associated with increased odds of using a condom at last intercourse (OR = 1.482, p = 0.10), decreased odds of sex with multiple partners (OR = 0.832, p = 0.036), and decreased odds of drug abuse (OR = 0.766, p = 0.034). Female workers randomly selected from five garment factories in Dhaka, Bangladesh Lower-Middle-Income (Sayem, 2010)
Cross-Sectional Age, education, monthly income, occupation, marital status, health status, sexual norms and other behaviors, rural residency, communist party membership Lifetime multiple sexual partnerships Blue-collar women vs. other womenC As compared with women in other occupations, the odds of having multiple sexual partners were increased among women in manual labor (OR = 3.347, 95% CI 1.069–10.476). China Health and Family Life Survey (CHFLS) of 2000 and 2006 Survey of Chinese People's Sexuality Upper-Middle-Income (Yingying et al., 2011)
Quasi-Experimental Displacement and firm closure Fertility Exposure-outcome among blue-collar women There were minimal effects of firm closure on fertility among blue-collar women for births in the three years following firm closure (ß = 0.002, SE = 0.016) and in the six years following firm closure (ß = - 0.013, SE = 0.023). Women in Austria affected by a firm closure compared to a control group of nondisplaced women. High-Income (Del Bono et al., 2012)
Cross-Sectional Age, gender, nationality, marital status, level of education, level of knowledge, level of attitude Use of condom in last sexual intercourse Male vs. female blue-collar workersC As compared with blue-collar men, the odds of using a condom during last sexual intercourse were increased among blue-collar women (OR = 8.790, 95% CI 2.009–38.467). Workers at eight different construction Sites in the Kathmandu Valley of Nepal. Low-Income (Pant et al., 2013)
Cross-Sectional Maternal age, birth weight, male fetal sex, smoking status, occupational class, induction, preeclampsia, gestational diabetes, maternal diabetes mellitus, fear of childbirth, placental abruption, placenta previa, in vitro fertilization, prior terminations, prior miscarriages, prior caesarean section, time period Planned Caesarian Section; Non-Planned Caesarian Section Blue-collar women vs. other women As compared to white-collar women, the odds of were increased for planned Caesarian section (OR = 1.11, 95% CI 1.03–1.19) and non-planned Caesarian section (OR = 1.19, 95% CI 1.13–1.25) among nulliparous blue-collar women. Among multiparous blue-collar women, the odds were also increased for planned C-section (OR = 1.14, 95% CI 1.08–1.22) and unplanned C-section (OR = 1.22, 95% CI 1.14–1.30). Population of Finland High-Income (Räisänen et al., 2014)
Case-Control Occupation Preterm birth Blue-collar women vs. other women As compared with women in office and administrative support occupations, the odds of preterm birth were increased among women in building and grounds cleaning (OR = 1.86, 95% CI 0.95–3.63) and among women in production occupations (OR = 1.43, 95% CI 0.83–2.45). University of California, Los Angeles, Environment and Pregnancy Outcomes Study High-Income (von Ehrenstein et al., 2014)
Cross-Sectional Years Exposed to Sulfur Dioxide Age at natural menopause; Early Menopause Exposure-outcome among blue-collar women Among blue-collar women, the rate of natural menopause was greatest among those with 21-25 years of sulfur dioxide exposure as compared to those with no sulfur dioxide exposure (HR = 1.290, 95% CI 1.035–1.608). The odds of early menopause were greatest among blue-collar women with 21–25 years of sulfur dioxide exposure as compared to those with no sulfur dioxide exposure (OR = 1.817, 95% CI 1.112–2.968). Jinchuan Cohort Upper-Middle-Income (Wang et al., 2015)
Respiratory Cross-Sectional Sex, age, smoking habits, atopy, familial asthma, SFEV1, airway protection Methacholine Responsiveness Male vs. female blue-collar workersC The odds of methacholine responsiveness among female potroom workers was increased as compared with male potroom workers (OR = 5.7, 95% CI 2.2–14.8). Potroom Workers at Ardal aluminum plant in Western Norway High-Income (Kongerud and Soyseth, 1991)
Cross-Sectional Smoking, age, height, weight, mill number, dust concentration Lung Function Exposure-outcome among blue-collar women Among European blue-collar women, increased dust concentration on the logarithmic scale was associated with decreased FEV1 (ß = -0.011, p > 0.05), increased FVC (ß = 0.022, p > 0.05), and a decreased FEV1:FVC (ß = -0.785, p < 0.05). Wool textile workers at five mills in West Yorkshire, United Kingdom High-Income (Love et al., 1991)
Cross-Sectional Cumulative smoking, Asian ethnicity, age, gender, not working in the weaving shed, total dust (pd1) Lung function Male vs. female blue-collar workersC As compared with female blue-collar workers FEV1 was increased among male blue-collar workers (ß = 4.9, SE = 1.09) and FVC was increased among male blue-collar workers (ß = 5.96, SE = 1.05). Workers at 16 mills in the Lancashire area of the United Kingdom. High-Income (Raza et al., 1999)
Cross-Sectional Sex, age, BMI, duration of employment, overall mean exposure to dust, current exposure to dust Vital capacity, FEV1 Male vs. female blue-collar workersC Among women, the observed mean vital capacity was 3.86 (SD 0.80) and among men the observed mean FEV1 was 3.20 (SE 0.76). Workers at two dolomite mines with adherent production facilities located in the Bergslagen area of central Sweden. High-Income (Seldén et al., 2001)
Case-Control Age, smoking status, occupational class, prior lung disease Adenocarcinoma of the lung; squamous cell and small cell carcinoma of the lung Blue-collar women vs. other womenA As compared with white-collar women, the odds were increased among blue-collar women for adenocarcinoma of the lung (OR = 1.85, 95% CI 1.26–2.72) and for squamous and small cell carcinoma of the lung (OR = 1.67, 95% CI 0.79–3.52). Hospital-Based Epidemiologic Research Program at Aichi Cancer Center (HERPACC) High-Income (Takezaki et al., 2001)
Cross-Sectional Gender Acute airway response Male vs. female blue-collar workersB The rate of acute airway response was increased among blue-collar women as compared with blue-collar men (HR = 1.51, 95% CI 1.01–2.24). Newly hired workers at one of three cotton mills close to Istanbul, Turkey. Upper-Middle-Income (Bakirci et al., 2007)
Longitudinal Occupational class Asthma Blue-collar women vs. other women Compared with female administrators, managers, and clerical workers, the risk of asthma was increased among all blue-collar women (RR = 1.4, 95% CI 1.2–1.6), female woodworkers (RR = 1.5, 95% CI 1.2–1.7), and other blue-collar women (RR = 1.4, 95% CI 1.2–1.6). All residents of Finland employed in wood-processing industries. High-Income (Heikkilä et al., 2008)
Cross-Sectional Occupational class; job exposure to gasses, fumes, dust or smoke; left or changed job because of respiratory symptoms Chronic Rhinosinusitis Blue-collar women vs. other womenC As compared with white-collar women, the odds of chronic rhinosinusitis were increased among blue-collar women (OR = 1.91. 95% CI 1.27–2.86). Global Asthma and Allergy European Network Study (GA2LEN) High-Income (Thilsing et al., 2012)
Longitudinal Welding Rhinitis Blue-collar women vs. other women The rate of rhinitis among female ever-welders was increased as compared with female never-welders (HR = 1.9, 95% CI 1.4–2.6). Respiratory Health in Northern Europe (RHINE) Study High-Income (Storaas et al., 2015)
Cross-Sectional Manganese Cumulative Exposure Index Pulmonary Function Exposure-outcome among blue-collar women No significant effect of Mn dust exposure on the pulmonary function was found in the female workers (all p>0.05). Guangxi Manganese-Exposed Workers Healthy Cohort (GXMEWHC) Upper-Middle-Income (Wang et al., 2015)
Self-Rated Health Cross-Sectional Occupational class Good health, poor health Blue-collar women vs. other womenC As compared with professional women, the odds of self-reported good health were increased among blue-collar women (OR = 1.39, 95% CI 1.05–1.83). The odds of self-reported poor health were similar in blue-collar women and professional women (OR = 0.98, 95% CI 0.95–1.47). Campbell National Health Monitor High-Income (Korda et al., 2002)
Cross-Sectional Occupational class Poor Self-Reported Health Blue-collar women vs. other women As compared with female professionals and managers, the odds of poor self-rated health were increased among blue-collar women (OR = 2.02, 95% CI 1.57–2.61). SUMER Study High-Income (Niedhammer et al., 2008)
Cross-Sectional Work hours per day, overtime, salary; exposure to chemicals and toxic vapors/substances, exposure to vibration and dangerous equipment, high temperatures, physical dangers/unhealthy conditions at work, poor air/ventilation, crowded workstations and uncomfortable working postures, having a safe work environment, adequate protective clothing and equipment, adequate work-related welfare facilities; psychological job demands/workload, work is interesting, company informs about its achievements, on-site training courses, resources/help and equipment availability, supervisor-related, discrimination - intimidation or threats; household income inadequacy, social and family working hours fit, adequate sanitary living conditions/potable water, children under 18. Health Status Exposure-outcome among blue-collar women As-compared with female professionals and managers, the odds of poor self-rated health were increased among blue-collar women (OR = 2.02, 95% CI 1.57–2.61). Blue-collar workers from 12 manufacturing companies in Lima, Perú. Upper-Middle-Income (Brunette et al., 2011)
Longitudinal Working class parents, household appliances, and poor somatic health at age 16; time in paid work, poor cash margin, and having children at age 21; months unemployment, financial strain, physically heavy work, high demands, low control, violence, and daily smoking at day 30 Somatic health Blue-collar women vs. other women As compared with white-collar women with good somatic health at age 30, the odds were decreased for poor somatic health at age 16 (OR = 0.53, 95% CI 0.23–1.24) blue-collar women with poor somatic health at age 30. As compared with white-collar women with good somatic health at age 30, the odds of daily smoking (OR = 2.72, 95% CI 1.22–6.06) were increased in blue-collar women with poor somatic health at age 30. Northern Swedish Cohort High-Income (Hammarström et al., 2011)
Quasi-Experimental Living wage Global self-rated health; comparative self-rated health Exposure-outcome among blue-collar women Odds of "excellent" or "much better" global self-rated health were increased among women in the intervention factory as compared with women the control factory (OR = 1.4, 95% CI 0.61–3.0). Odds of "excellent" or "much better" comparative self-rated health were increased among women in the intervention factory as compared with women the control factory (OR = 1.4, 95% CI 1.4–6.7). Workers at intervention and control factories in the Dominican Republic Upper-Middle-Income (Landefeld et al., 2014)
Smoking & Other Substance Use Longitudinal Gender, race, age, alcohol, burnout, job problems, unwind time, years driving Smoking increase, initiation, and maintenance Male vs. female blue-collar workersB The odds of smoking were decreased among male bus drivers as compared with female bus drivers (OR = 0.60, 95% CI 0.33–1.09). San Francisco MUNI Health and Safety Study High-Income (Cunradi et al., 2007)
Cross-Sectional Job demand and control; effort and reward; over commitment; physical demand; shift work; working hours; job pressure Current Smoking; Former Smoking Exposure-outcome among blue-collar women Both job strain and passive jobs were associated with smoking among blue-collar women (OR = 2.6 and 3.7, respectively), and physical demand was associated with smoking among blue-collar women (OR = 16.8). No confidence intervals reported. A random sample of White Pages listings in the sate of Victoria in Australia. High-Income (Radi et al., 2007)
Longitudinal Young adult socioeconomic position (education and occupational class); family socioeconomic position; family structure; family connectedness; smoker in home; easy access to cigarettes; high school; CES-D; number of friends who smoke; smoked during adolescence Heavy and Light-to-Moderate Smoking Blue-collar women vs. other women As compared to women with further education, the odds were increased among blue-collar women for light-to-moderate smoking (OR = 1.88, 95% CI 1.28–2.78), heavy smoking (OR = 3.12, 95% CI 2.10–4.63). As compared to women with further education, there was no difference in odds of overweight among blue-collar women (OR = 1.04, 95% CI 0.49–2.21) and the odds of overweight were decreased (OR = 0.74, 95% CI 0.29–1.85). National Longitudinal Study of Adolescent Health High-Income (Yang et al., 2008)
Longitudinal Partner cessation request, partner smoking, intervention group, age, gender, race/ethnicity, education, income, smoking quantity (per day) Smoking Abstinence at 1- and 6-Months Post Intervention Follow-Up Male vs. female blue-collar workersB As compared with men, the odds of smoking at baseline among women were 0.36 times the odds of smoking at baseline among men (0.14, 0.91). The OR for smoking one-month post-intervention in women versus men was 0.71 (0.23–2.18) and the OR for prolonged cessation (six-months after baseline) in women vs. men was 1.19 (0.25, 5.80). The MassBUILT Study High-Income (Okechukwu et al., 2010)
Cross-Sectional Age, marital status, education, age at first sexual exposure, sources of knowledge on HIV/AIDS Use of condoms; sex with multiple partners; drug abuse Exposure-outcome among blue-collar women Among female garment workers, increased knowledge score was associated with increased odds of using a condom at last intercourse (OR = 1.482, p = 0.10), decreased odds of sex with multiple partners (OR = 0.832, p = 0.036), and decreased odds of drug abuse (OR = 0.766, p = 0.034). Female workers randomly selected from five garment factories in Dhaka, Bangladesh High-Income (Sayem, 2010)
Cross-Sectional Gender, age, education, ethnicity, negative affectivity, social desirability, drinking norms, policy enforcement, role overload, job insecurity, job hazards, decision involvement, self-estrangement, social interactions Quantity of alcohol consumption; Frequency of alcohol consumption; Frequency of Drug Use Male vs. female blue-collar workersB As compared with blue-collar men, blue-collar women consumed greater quantities of alcohol (ß = 0.161, p > 0.05), consumed alcohol with greater frequency (ß = 0.260, p > 0.050), and used drugs less frequently (ß = -0.845, p < 0.01). Random sample of workers from a single manufacturing firm in Israel employed at plants with 80 or more workers. High-Income (Biron et al., 2011)
Longitudinal Working class parents, household appliances, and poor somatic health at age 16; time in paid work, poor cash margin, and having children at age 21; months unemployment, financial strain, physically heavy work, high demands, low control, violence, and daily smoking at day 30 Somatic health Blue-collar women vs. other women As compared with white-collar women with good somatic health at age 30, the odds were decreased for poor somatic health at age 16 (OR = 0.53, 95% CI 0.23–1.24) blue-collar women with poor somatic health at age 30. As compared with white-collar women with good somatic health at age 30, the odds of daily smoking (OR = 2.72, 95% CI 1.22–6.06) were increased in blue-collar women with poor somatic health at age 30. Northern Swedish Cohort High-Income (Hammarström et al., 2011)
Cross-Sectional Age, gender, race, income, education, self-rated health, occupational factors, union commitment, job satisfaction, exposure to occupational factors Current smoking Male vs. female blue-collar workersB The odds of current smoking among blue-collar women were increased as compared with blue-collar men (OR = 1.37, 95% CI 0.87–2.17). The MassBUILT Study High-Income (Chin et al., 2012)
Longitudinal Intervention status, age, gender, race, income, education, smoking intensity, union commitment, exposure to occupational hazards, concern about exposure to occupational hazards Smoking Cessation at 1- and 6-months post-intervention monitoring Male vs. female blue-collar workersB Odds of smoking cessation one-month post-intervention were increased among blue-collar women as compared with blue-collar men (OR = 2.19, 95% CI 0.61–7.89). Odds of prolonged cessation six months post-intervention among blue-collar women were increased as compared with blue-collar men (OR = 1.20, 95% CI 0.24 – 6.06). The MassBUILT Study High-Income (Chin et al., 2012)
Cross-Sectional Age, gender, race, income, education, time to first cigarette, age of smoking initiation, intention to quit at 30 days, self-efficacy for quitting 30 days, temptation to smoke, decisional balance, household smoking, dust exposure at work, chemical exposure at work, concern about exposure to occupational hazards Heavy smoking Male vs. female blue-collar workersC The odds of heavy smoking among blue-collar men were increased as compared with blue-collar women (OR = 4.55, 95% CI 1.62–12.79). The MassBUILT Study High-Income (Chin et al., 2013)
Cross-Sectional Occupational class, education, poverty-income ratio Smoking Blue-collar women vs. other women The odds of smoking among blue-collar women were increased as compared with white-collar women (6.65, 95% CI 4.90–9.03). Third Korean National health and Nutrition Examination (KNHANES III) High-Income (Cho and Lee, 2012)
Cross-Sectional Occupational class, exposure to workplace environmental tobacco smoke (ETS) Never smoking; Exposure to workplace ETS; Smoking cessation; Smoking intensity Blue-collar women vs. other women The odds of being a never-smoking were similar among blue-collar women and female managers and professionals (OR = 1.08, 95% CI 0.75–1.55). The odds of workplace environmental tobacco smoker were increased among blue-collar women as compared with female managers and professionals (OR = 1.53, 95% CI 1.01–2.30). Multi-Ethnic Study of Atherosclerosis (MESA) High-Income (Fujishiro et al., 2012)
Cross-Sectional Age, sex, past month cigarette use, AUDIT (alcohol problem), race, education Smokeless Tobacco Use Male vs. female blue-collar workersC The odds of smokeless tobacco use were increased among male operating engineers as compared with female operating engineers (OR = 5.06, 95% CI 0.66–38.75). Operating engineers coming to either an apprentice certification or Hazardous Materials (Hazmat) refresher course in Michigan. High-Income (Noonan and Duffy, 2012)
Longitudinal Labor market shock, cigarette prices, state anti-smoking sentiment, age, sex, education, race/ethnicity, employment status, family income, data collection year Smoking status Male vs. female blue-collar workersB The odds of smoking among female construction workers were increased as compared with male construction workers (OR = 1.08, 95% CI 0.90 1.29). Tobacco Use supplement to the Current Population Survey (TUS-CPS) High-Income (Okechukwu et al., 2012)
Cross-Sectional Occupational class, race/ethnicity, age, education, adverse childhood events Monthly binge drinking, past-30 day smoking, past year marijuana use, polysubstance use (2+ more) Blue-collar women vs. other women As compared with women employed in non-physically demanding occupations, the odds were increased for binge drinking (OR = 4.01, 95% CI 1.68–9.49), past 30-day smoking (OR = 1.94, 95% CI 1.18–3.21), marijuana use (OR = 1.37, 95% CI 0.59–3.20), and polysubstance use (OR = 3.21, 95% CI 1.40–7.38) among blue-collar women. A purposive sample of workers and their spouses or cohabitating partners sampled from a large union representing construction industry workers in Northern California. High-Income (Cunradi et al., 2014)
Cross-Sectional Occupational class; occupational status (high, upper, intermediate, simple, low); employment status Cigarette smoking, heavy smoking; alcohol consumption, excessive heavy drinking; cannabis use; analgesic use; weekly analgesics use Blue-collar women vs. other women As compared with white-collar women, the 30-day prevalence of smoking (OR = 1.10, 95% CI = 0.88–1.38); the odds of smoking ≥ 20 cigarettes per day in the past 30 days (OR = 1.15, 95% CI 0.70–1.90); the 12-month prevalence of cannabis use (OR = 1.08, 95% CI 0.56–2.10); and the 30-day prevalence of analgesic use (OR = 1.23, 95% CI 0.93 - 1.63) were increased among blue-collar women. The 30-day prevalence of alcohol consumption (OR = 0.74, 95% CI 0.60 - 0.92); the 30-day prevalence of excessive heavy drinking (OR = 0.78, 95% CI 0.59 - 1.03), and the 30-day prevalence of analgesic use (OR = 0.88. 95% CI 0.73–1.07) were decreased among blue-collar women as compared with white-collar women. German Epidemiological Survey of Substance Use High-Income (Maron et al., 2016)
*

NR = Not Reported.

**

We included only those independent variables for which authors reported results.

A

Denotes studies in which occupational class was not the primary independent variable.

B

Denotes studies in which sex/gender was not the primary independent variable.

C

Denotes exploratory studies that considered multiple independent variables simultaneously.