Mortality and morbidity study of petrochemical employees in a polluted site

Abstract

BackgroundThe area of Gela was included among the 57 Italian polluted sites of national interest for environmental remediation because of its widespread contamination from a petrochemical complex. The present study investigates mortality and morbidity of the cohort of Gela petrochemical workers with the aim of disentangling occupational from residential risk.MethodsMortality was assessed for 5,627 men hired from 1960, year of the plant start-up, to 1993; it was followed up for vital status in the period 1960–2002. Morbidity was analysed for 5,431 workers neither dead nor lost to follow-up from 1960 to 2001 and was based on Hospital Discharge Records in the period 2001–2006. The work experience was classified in terms of job categories such as blue collars, white collars, and both – workers who shifted from blue to white collar (95%) or vice versa. An ad hoc mobility model was applied to define qualitative categories of residence in Gela, as residents and commuters. Standardized Mortality Ratios (SMRs) and Mortality Rate Ratios (MRRs) were computed, the latter by using a Poisson regression model. Morbidity was analyzed in terms of Hospital Discharge Odds Ratios (HDORs) through a logistic regression model. While performing the internal comparisons, white collars was the reference category for the job analysis, and commuters was the reference category for the residential analysis.ResultsIn the light of epidemiological evidence about health risk from petrochemical industries in both occupational and environmental settings, and/or on the basis of information about occupational and residential contamination and health risk in the area of Gela, noteworthy results are shown for lung cancer [MRR: 2.11 (CI 90%; 0.96-4.63) in blue collars; 1.71 (1.09-2.69) in residents], respiratory diseases [HDOR: 2.0 (1.0-3.0) in blue collars; 1.4 (0.96-2.06) in residents] and genitourinary diseases [HDOR: 1.34 (1.06-1.68) in blue collars; 1.23 (1.04-1.45) in residents].ConclusionsThe results support a role of the exposures in the occupational and residential settings, the latter due to the local ascertained contamination, in affecting the workers’ health. These results underline the urgent need of water, soil, air and food-chain monitoring programs, to discover active sources of exposure and consequently define public health interventions.

Regional inequalities in benzene exposures across the European petrochemical industry: A Bayesian multilevel modelling approach

“…In contrast, Knox (1994) identified the number of childhood leukaemia incidences to increase by 26% if residing within 5 km of an oil refinery, or 25% for those within 1.25 km of a downstream facility. While the debate has stifled in the UK, interest has peaked in recent years across other European nations, particularly in France and Italy (Pasetto et al, 2012; Pascal et al, 2013; Bentayeb et al, 2015; Fazzo et al, 2016). A 25-year study of 20,327 French residents, identified an 8% increase in mortalities per 1 μg/m 3 increase in the annual average concentration of benzene, after adjusting for pollutants from non-industrial sources (Bentayeb et al, 2015).…”

Residents’ Self-Reported Health Effects and Annoyance in Relation to Air Pollution Exposure in an Industrial Area in Eastern-Estonia

“…In Italy, an analysis of 44 industrially contaminated sites showed an excess of deaths [ 16 ] and an analysis of 17 sites showed an increase in the incidence of cancer [ 17 ]. In the area surrounding a large oil refinery, thermoelectric power plant, and petrochemical plants in Gela, Italy, people are at a high risk of lung cancer, and non-malignant respiratory and genitourinary diseases [ 18 ]. In Taranto, close to a large steel plant, a refinery, the harbour, and waste dumps, an excess risk of total deaths, all kinds of cancers, lung cancer, and cardiovascular and respiratory diseases, both acute and chronic, have been reported [ 19 ].…”

Lung cancer mortality of residents living near petrochemical industrial complexes: a meta-analysis

“…Some studies further adjusted ethnicity, socioeconomic levels (e.g., school levels, job collars categories, unemployment, number of family members, overcrowding, and ownership of dwellings), or study periods. Four studies had full score of nine stars [ 18 , 21 – 23 ]; two had 8 out of 9 stars [ 7 , 8 ]; and one study had seven out of nine stars [ 19 ] (see Additional file 2 ).…”

“…First, the definition of exposure varied slightly between studies. Most studies defined the exposure based on the geographical locations or distances of residencies from PIC [ 7 , 8 , 19 , 21 – 23 ], while one study compared the exposed group and reference group by matching job categories in PIC and non-PIC towns [ 18 ]. Misclassification of exposure and non-exposure might exist and bias the pooled estimates towards the null.…”

“…Considering that a study population might appear in different articles, we selected the latest article to avoid bias towards the specific population. Finally, we included seven articles that reported 13 estimates for meta-analysis: three articles reporting sex-specific mortality rate ratios of lung cancer [ 7 , 18 , 21 ]; one article reporting sex-specific age-adjusted mortality rates of industrial corridors and Louisiana, respectively [ 19 ]; one article reporting odds ratios for both sexes combined [ 22 ]; one article reporting standardized mortality ratios by sex [ 8 ] and another one reporting standardized mortality ratios sex combined [ 23 ]. The ratio of Belli’s study was regarded as for males in subgroup analysis because male accounted for 84% of the study group [ 22 ].…”