Social deprivation and hospital admission for respiratory infection: an ecological study

Respir Med. 2003 Nov;97(11):1219-24. doi: 10.1016/s0954-6111(03)00252-x.


Study objective: To examine the relationship between social deprivation and risk of hospital admission for respiratory infection.

Methods and subjects: Ecological study using hospital episode statistics and population census data. Cases were residents of the West Midlands Health Region admitted to hospital with a diagnosis of respiratory infection, acute respiratory infection, pneumonia or influenza over a 5-year period. Postcodes of cases were used to assign Townsend deprivation scores; these were then ranked and divided into five deprivation categories. Poisson regression analysis was used to estimate the magnitude of effect for associations between deprivation category and hospital admission by age and admitting diagnosis.

Main results: There were 136755 admissions for respiratory infection, equivalent to an annual admission rate of 27.1 per 1000 population (95% CI = 26.9-27.2). Deprivation was associated with increased admission rates for all respiratory infection (P < 0.0001) and affected all age-groups. The greatest effect was in the 0-4 years age-group with admission rates 91% higher in the most deprived children compared to the least deprived. Hospital admissions for acute respiratory infection and pneumonia were both significantly associated with deprivation (P < 0.0001).

Conclusions: Respiratory infection is associated with social inequalities in all age-groups, particularly in children. Prevention of respiratory infection could make an important contribution to reducing health inequalities.

MeSH terms

  • Acute Disease
  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Child
  • Child, Preschool
  • England / epidemiology
  • Hospitalization / statistics & numerical data*
  • Humans
  • Incidence
  • Infant
  • Infant, Newborn
  • Influenza, Human / epidemiology
  • Middle Aged
  • Pneumonia / epidemiology
  • Poverty / statistics & numerical data*
  • Poverty Areas
  • Regression Analysis
  • Respiratory Tract Infections / epidemiology*