title: The use of conjoint analysis to elicit community preferences in public health research: a case study of hospital services in South Australia.
abstract: OBJECTIVE
  To demonstrate the use of conjoint analysis (CA) in public health research through a survey of the South Australian community about aspects of their public hospital services.
  
  
  METHODS
  A series of focus groups determined the most important attributes in choice of hospital services. These were built into a CA survey, using the discrete choice approach. The survey was posted to a representative sample of 700 South Australians. Theoretical validity, internal consistency and non-response bias were all investigated.
  
  
  RESULTS
  Some 231 individuals returned the questionnaire. The attribute, 'improvement in complication rates' was positively associated with choice of hospital. Three attributes were found to be negatively associated with such choice: 'waiting times for casualty', 'waiting times for elective surgery' and, anomalously, 'parking and transport facilities'. 'Travel time' and the cost attribute, 'Medicare levy' were not statistically significant. Trade-offs between the significant attributes were estimated, as were satisfaction or utility scores for different ways of providing hospital services. Results concerning internal consistency and internal validity were encouraging, but some potential for non-response bias was detected.
  
  
  CONCLUSIONS
  A high premium is placed on the quality of hospital care and members of the community are prepared to choose between hospitals largely on the basis of outcomes and length of waiting times for elective surgery and in casualty.
  
  
  CONCLUSIONS
  CA can yield potentially policy-relevant information about community preferences for health services.
authors.affiliation: Department of Public Health and Community Medicine, University of Sydney, New South Wales. stephenj@pub.health.usyd.edu.au
authors.forename: S
authors.initials: S
authors.lastname: Jan
authors.forename: G
authors.initials: G
authors.lastname: Mooney
authors.forename: M
authors.initials: M
authors.lastname: Ryan
authors.forename: K
authors.initials: K
authors.lastname: Bruggemann
authors.forename: K
authors.initials: K
authors.lastname: Alexander
mesh_terms: D000328:Adult; D015982:Bias; D002755:Choice Behavior; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D005260:Female; D017144:Focus Groups; D006302:Health Services Research / Q000379:methods*; D006779:Hospitals, Public / Q000592:standards*; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017060:Patient Satisfaction / Q000706:statistics & numerical data*; D011634:Public Health; D015203:Reproducibility of Results; D013021:South Australia; D011795:Surveys and Questionnaires