title: Illness absence patterns among employees in a petrochemical facility: impact of selected health risk factors.
abstract: BACKGROUND
  Illness absence in a working population is a complex phenomenon and can be influenced by many factors, such as age, gender, and personal health risk factors. The current study used prospectively collected employee health risk and morbidity data to examine illness absence patterns for a working population and to quantify the impact of selected health risk factors on employee illness absence.
  
  
  METHODS
  The study population consisted of 2550 regular employees working at a Texas petrochemical facility. Morbidity data were extracted from the company's Health Surveillance System, and records of absences were derived from personnel and payroll systems. The morbidity frequency rate and average duration of absence per employee per year were calculated by age, gender, and selected health risk factors, including smoking, body mass index, cholesterol, triglycerides, hypertension, and fasting glucose.
  
  
  RESULTS
  Morbidity frequency rates and average duration of absence increased with age and with the presence of health risk factors. The absence frequency rate increased with an increase in the number of risk factors present from no risk factors (11.8 per 100 employees) to four or more risk factors (32.3 per 100 employees). The number of workdays lost also increased with the number of risk factors present, with the least number of workdays lost by employees with zero risk factors (4.1 day), followed by one (6.4 days), two (8.8 days), three (9.3 days), and four or more risk factors (12.6 days).
  
  
  CONCLUSIONS
  The impact of employee health risk factors has been shown in this study to be associated with absence frequency and duration of absence. Reduction in employee health risk factors can be an effective means of improving employees' health and increasing a company's productivity.
authors.affiliation: Shell Oil Company, Houston, TX 77252-2463, USA. shan.tsai@shell.com
authors.forename: Shan P
authors.initials: SP
authors.lastname: Tsai
authors.forename: Judy K
authors.initials: JK
authors.lastname: Wendt
authors.forename: Farah S
authors.initials: FS
authors.lastname: Ahmed
authors.forename: Robin P
authors.initials: RP
authors.lastname: Donnelly
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Strawmyer
mesh_terms: D000041:Absenteeism; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001786:Blood Glucose / Q000032:analysis; D002617:Chemical Industry; D015897:Comorbidity; D016208:Databases, Factual; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D016272:Occupational Health / Q000706:statistics & numerical data*; D010578:Petroleum; D012307:Risk Factors; D018571:Sentinel Surveillance; D017678:Sex Distribution; D018582:Sick Leave / Q000706:statistics & numerical data*; D016540:Smoking Cessation / Q000706:statistics & numerical data; D013781:Texas / Q000453:epidemiology; D000078329:Workforce
chemical_list: D001786:Blood Glucose; D010578:Petroleum