title: Impact of pre-existing chronic conditions on age differences in sickness absence after a musculoskeletal work injury: a path analysis approach.
abstract: OBJECTIVE
  This study aims to examine the extent to which a greater prevalence of pre-existing chronic conditions among older workers explains why older age is associated with longer duration of sickness absence (SA) following a musculoskeletal work-related injury in British Columbia.
  
  
  METHODS
  A secondary analysis of workers' compensation claims in British Columbia over three time periods (1997-1998; 2001-2002, and 2005-2006), the study comprised 102 997 and 53 882 claims among men and women, respectively. Path models examined the relationships between age and days of absence and the relative contribution of eight different pre-existing chronic conditions (osteoarthritis, rheumatoid arthritis, hypertension, coronary heart disease, diabetes, thyroid conditions, hearing problems, and depression) to this relationship. Models were adjusted for individual, injury, occupational, and industrial covariates.
  
  
  RESULTS
  The relationship between age and length of SA was stronger for men than women. A statistically significant indirect effect was present between older age, diabetes, and longer days of SA among both men and women. Indirect effects between age and days of SA were also present through osteoarthritis, among men but not women, and coronary heart disease, among women but not men. Depression was associated with longer duration of SA but was most prevalent among middle-aged claimants. Approximately 70-78% of the effect of age on days of SA remained unexplained after accounting for pre-existing conditions.
  
  
  CONCLUSIONS
  Pre-existing chronic conditions, specifically diabetes, osteoarthritis and coronary heart disease, represent important factors that explain why older age is associated with more days of SA following a musculoskeletal injury. Given the increasing prevalence of chronic conditions among labor market participants (and subsequently injured workers) moderate reductions in age differences in SA could be gained by better understanding the mechanisms linking these conditions to longer durations of SA.
authors.affiliation: School of Public Health and Preventive Medicine, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria, 3004, Australia. peter.smith@monash.edu.
authors.forename: Peter
authors.initials: P
authors.lastname: Smith
authors.forename: Amber
authors.initials: A
authors.lastname: Bielecky
authors.forename: Selahadin
authors.initials: S
authors.lastname: Ibrahim
authors.forename: Cameron
authors.initials: C
authors.lastname: Mustard
authors.forename: Ron
authors.initials: R
authors.lastname: Saunders
authors.forename: Dorcas
authors.initials: D
authors.lastname: Beaton
authors.forename: Mieke
authors.initials: M
authors.lastname: Koehoorn
authors.forename: Chris
authors.initials: C
authors.lastname: McLeod
authors.forename: Heather
authors.initials: H
authors.lastname: Scott-Marshall
authors.forename: Sheilah
authors.initials: S
authors.lastname: Hogg-Johnson
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001955:British Columbia / Q000453:epidemiology; D002908:Chronic Disease; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009140:Musculoskeletal Diseases / Q000191:economics / Q000453:epidemiology*; D060051:Occupational Injuries / Q000191:economics / Q000453:epidemiology*; D064266:Preexisting Condition Coverage / Q000191:economics / Q000706:statistics & numerical data*; D015995:Prevalence; D012737:Sex Factors; D018582:Sick Leave / Q000191:economics / Q000706:statistics & numerical data*; D013997:Time Factors; D014942:Workers' Compensation / Q000706:statistics & numerical data*; D055815:Young Adult