[Risk factors for absenteeism due to sick leave in the petroleum industry].
OBJECTIVE
To identify risk factors for absenteeism among workers with sick leave in an oil company.


METHODS
A case-control study (120 cases and 656 controls) nested in a retrospective cohort study following up all employees of an oil company in the North-Northeast of Brazil from 2007 to 2009. The response variable used to represent absenteeism with sick leave was the average incidence of sick leave, defined as the ratio between total sick days and potential working days in the period. Logistic regression techniques were used to investigate the association between average incidence of sick leave > 5.0% over the period and the variables sex, position, age, time at work, shift work, smoking, arterial hypertension, body mass index, physical activity, coronary risk, sleep, glycemia, non-managed diabetes, cardiovascular, digestive, musculoskeletal, neurological and neoplastic diseases, straining body positioning during work, satisfaction at work, relationship with management, and concentrated attention at work.


RESULTS
Average incidence of sick leave higher than 5.0% in the cohort period was 15.5%. The logistic model revealed that workers with average incidence of sick leave higher than 5.0% were 2.6 times more likely to be female; 2.0 time more likely to be smokers; 1.8 time more likely to be former smokers; 2.2 times more likely to report abnormal sleep and 10.5 times more likely to report dissatisfaction with their than workers with average incidence of sick leave ≤ 5.0% in the period.


CONCLUSIONS
In this population, female gender, being a smoker or a former smoker, reporting dissatisfaction with the job and reporting abnormal sleep are good predictors of occupational absenteeism with sick leave.


OBJECTIVE
To identify risk factors for absenteeism among workers with sick leave in an oil company.


METHODS
A case-control study (120 cases and 656 controls) nested in a retrospective cohort study following up all employees of an oil company in the North-Northeast of Brazil from 2007 to 2009. The response variable used to represent absenteeism with sick leave was the average incidence of sick leave, defined as the ratio between total sick days and potential working days in the period. Logistic regression techniques were used to investigate the association between average incidence of sick leave > 5.0% over the period and the variables sex, position, age, time at work, shift work, smoking, arterial hypertension, body mass index, physical activity, coronary risk, sleep, glycemia, non-managed diabetes, cardiovascular, digestive, musculoskeletal, neurological and neoplastic diseases, straining body positioning during work, satisfaction at work, relationship with management, and concentrated attention at work.


RESULTS
Average incidence of sick leave higher than 5.0% in the cohort period was 15.5%. The logistic model revealed that workers with average incidence of sick leave higher than 5.0% were 2.6 times more likely to be female; 2.0 time more likely to be smokers; 1.8 time more likely to be former smokers; 2.2 times more likely to report abnormal sleep and 10.5 times more likely to report dissatisfaction with their than workers with average incidence of sick leave ≤ 5.0% in the period.


CONCLUSIONS
In this population, female gender, being a smoker or a former smoker, reporting dissatisfaction with the job and reporting abnormal sleep are good predictors of occupational absenteeism with sick leave.
Nágila Soares Xavier
NS
Oenning
Fernando Martins
FM
Carvalho
Veronica Maria Cadena
VM
Lima
D000041:Absenteeism; D000328:Adult; D000368:Aged; D001938:Brazil / Q000453:epidemiology; D015331:Cohort Studies; D019652:Extraction and Processing Industry / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009784:Occupational Diseases / Q000453:epidemiology*; D010578:Petroleum; D012307:Risk Factors; D012737:Sex Factors; D018582:Sick Leave / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D055815:Young Adult
D010578:Petroleum