A multifactorial approach to sickness absenteeism among nursing staff

Rev Saude Publica. 2012 Apr;46(2):259-68. doi: 10.1590/s0034-89102012005000018. Epub 2012 Feb 24.
[Article in English, Portuguese]

Abstract

Objective: To analyze factors associated with self-reported sickness absenteeism among nursing workers.

Methods: Cross-sectional study with 1,509 workers from three public hospitals in the city of Rio de Janeiro (Southeastern Brazil) in 2006. Absenteeism was classified in three levels: no day, a few days (1-9 days) and many days (> 10 days), based on the answer to a question of the work ability index questionnaire. The logistic regression analysis considered a conceptual model based on distal (socioeconomic status), intermediate I (occupational characteristics), intermediate II (lifestyle characteristics), and proximal (diseases and health conditions) determinants.

Results: The frequencies of sickness absenteeism were 20.3% and 16.6% for a few days and many days, respectively. Those who reported more than one job, musculoskeletal diseases and rated their health as poor or regular had higher odds of absenteeism. Compared to nurses, nursing assistants were less likely to mention a few days, and technicians were more likely to have many days of absence. Higher odds of mentioning many days of absence were observed among public servants, compared to contract workers (OR = 3.12; 95%CI 1.86;5.22), and among married (OR = 1.73; 95%CI 1.14;2.63) and separated, divorced and widowed individuals (OR = 2.06, 95%CI 1.27;3.35), compared to singles.

Conclusions: Different variables were associated with the two forms of absenteeism, which suggests its multiple and complex determination related to factors from different levels that cannot be exclusively explained by health problems.

MeSH terms

  • Absenteeism*
  • Attitude of Health Personnel
  • Brazil / epidemiology
  • Cross-Sectional Studies
  • Female
  • Health Status*
  • Hospitals, Public
  • Humans
  • Job Satisfaction
  • Male
  • Nursing Staff, Hospital / psychology
  • Nursing Staff, Hospital / statistics & numerical data*
  • Occupational Diseases / epidemiology*
  • Occupational Health
  • Regression Analysis
  • Self Report*
  • Sex Factors
  • Sick Leave / statistics & numerical data*
  • Socioeconomic Factors