Reproductive outcome in a cohort of male and female rubber workers: a registry study

Abstract

Purpose To investigate whether blue-collar employment in the Swedish rubber industry from 1973 onwards had a negative impact on reproductive health. Methods Pairs of mother and child, and triads of fathermother-child were obtained through linkage of a cohort of 18,518 rubber factory employees with the Swedish Population Registry. Birth outcomes were obtained from the Medical Birth Register for 17,918 children. For each child, parental employment as blue-collar rubber worker during the pregnancy and sperm maturation period was obtained from work-place records. Children to female food industry workers, in all 33,256, constituted an external reference group. Results The sex ratio was reversed, with odds ratio (OR) for having a girl was 1.15 (95% CI 1.02, 1.31) when the mother was exposed. When both parents were exposed, the OR was even higher, 1.28 (95% CI 1.02, 1.62). An increased risk of multiple births was observed when both parents were exposed, with OR 2.42 (95% CI 1.17, 5.01). Children with both maternal and paternal exposure had a reduced birth weight compared to the external reference cohort. After adjustment for smoking (available for births from 1983 onwards), ethnicity and sex, the diVerence between children (singletons, live births) with maternal and paternal exposure and external referents was ¡142 g (95% CI ¡229, ¡54). The adjusted OR for having a small-forgestational-age child was 2.15 (95% CI 1.45, 3.18) when the mother was a rubber worker during the pregnancy. Conclusion There were clear indications that reproductive outcome was adversely aVected in rubber workers. The Wndings warrant further investigation with reWnement of exposure indices and inclusion of other endpoints of reproductive health.

Blue-collar work and women's health: A systematic review of the evidence from 1990 to 2015

“…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).…”

“…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).…”

“…Studies that focused on identifying risk factors for morbidity and mortality among blue-collar women typically focused either on the physical risks associated with blue-collar, job demand, or organizational climate. Several studies identified chemical exposures a risk factor not only for cancers among blue-collar women ( Hwang and Lee, 2014 , Iverson and Erwin, 1997 , Hrubá et al, 2009 , Hwang et al, 2015 ), but also for psychiatric distress and depression ( Jousilahti et al, 1996 , Jakobsson and Mikoczy, 2009 ). Increased job demand, job conflict, subjective monotony, skill underutilization and sexual harassment were all identified as risk factors for psychiatric distress ( Juutilainen et al, 2000 , Kaergaard and Andersen, 2000 , Kaila-Kangas et al, 2006 ), although reduced psychological distress was observed among blue-collar women with adequate social support ( Jousilahti et al, 1996 , Kalleberg, 2009 ).…”

“…Among blue-collar women, studies linked psychological distress to work-related physical and psychological stressors including sexual harassment ( Jousilahti et al, 1996 , Kalleberg, 2009 , Juutilainen et al, 2000 , Santhi et al, 2016 , Sakr et al, 2010 , Kaila-Kangas et al, 2006 , Jakobsson and Mikoczy, 2009 ); domestic arrangements ( Santos and Barros, 2003 , Juutilainen et al, 2000 , Santhi et al, 2016 ); and individual-level factors such as self-esteem, smoking, and BMI ( Jousilahti et al, 1996 , Sakr et al, 2010 , Santhi et al, 2016 ). Reduced psychological distress was observed among blue-collar women with adequate social support ( Jousilahti et al, 1996 , Kalleberg, 2009 ).…”

“…Other health outcomes studied included work-related fatigue, insomnia, melatonin production, and sleep quality ( Won et al, 2013 , Juutilainen et al, 2000 , World Bank, 2018 , Westreich and Greenland, 2013 , Raza et al, 1999 ); fibromyalgia ( Cobankara, Unal, Kaya, Bozkurt, & Ozturk, 2011 ); headache, cognition, and other neurobehavioral effects ( Jousilahti et al, 1996 , Juutilainen et al, 2000 , Yang et al, 2008 , Jakobsson and Mikoczy, 2009 , Yingying et al, 2011 ); serum uric acid production ( Shirom, Melamed, & Nir-Dotan, 2000 ); blood and bone lead levels ( Potula and Kaye, 2006 ); tooth abrasion ( Kovacevic and Belojevic, 2006 ); hearing loss ( Nguyen et al, 1998 ); and the prevalence long and short-term health conditions ( Korda et al, 2002 ).…”