Risk factors for asthma deaths: a population-based, case-control study

Aust N Z J Public Health. 1999 Dec;23(6):595-600. doi: 10.1111/j.1467-842x.1999.tb01543.x.


Objectives: To investigate risk factors for death from asthma using a case-control study design with two control groups.

Methods: Cases (n = 42) comprised subjects aged 10-59 years who died from asthma. Two control groups were selected: a random sample of asthmatics from the community (n = 132) and age and sex matched patients recently admitted to hospital for asthma (n = 89). We obtained information from proxies of cases and controls, and their general practitioners, by a structured telephone survey. Matched and unmatched logistic regression analyses were used to determine odds ratios for risk factors for asthma deaths.

Results: Compared to community controls, important risk factors for asthma deaths included indicators of asthma severity, use of three or more groups of asthma medications, more extensive use of health services for asthma, poor compliance with asthma medications and regularly missing hospital and general practitioner appointments for asthma. Compared to hospital controls, risk factors for asthma deaths were previous visits to emergency department for asthma, knowledge about asthma medications and regularly missing general practitioner appointments.

Conclusions: In this study, severity of asthma, increased health service utilisation and suboptimal asthma self-management were associated with increased risks for asthma death.

Implications: People with severe asthma or poorly controlled asthma have a greater risk of dying from their asthma. Both clinicians and non-clinicians managing asthma should regularly assess the appropriateness of management to prevent deaths.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Asthma / mortality*
  • Case-Control Studies
  • Cause of Death*
  • Child
  • Cohort Studies
  • Confidence Intervals
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • New South Wales / epidemiology
  • Risk Factors
  • Sampling Studies
  • Sex Distribution
  • Software