title: Communicating population health statistics through graphs: a randomised controlled trial of graph design interventions.
abstract: BACKGROUND
  Australian epidemiologists have recognised that lay readers have difficulty understanding statistical graphs in reports on population health. This study aimed to provide evidence for graph design improvements that increase comprehension by non-experts.
  
  
  METHODS
  This was a double-blind, randomised, controlled trial of graph-design interventions, conducted as a postal survey. Control and intervention participants were randomly selected from telephone directories of health system employees. Eligible participants were on duty at the listed location during the study period. Controls received a booklet of 12 graphs from original publications, and intervention participants received a booklet of the same graphs with design modifications. A questionnaire with 39 interpretation tasks was included with the booklet. Interventions were assessed using the ratio of the prevalence of correct responses given by the intervention group to those given by the control group for each task.
  
  
  RESULTS
  The response rate from 543 eligible participants (261 intervention and 282 control) was 67%. The prevalence of correct answers in the control group ranged from 13% for a task requiring knowledge of an acronym to 97% for a task identifying the largest category in a pie chart. Interventions producing the greatest improvement in comprehension were: changing a pie chart to a bar graph (3.6-fold increase in correct point reading), changing the y axis of a graph so that the upward direction represented an increase (2.9-fold increase in correct judgement of trend direction), a footnote to explain an acronym (2.5-fold increase in knowledge of the acronym), and matching the y axis range of two adjacent graphs (two-fold increase in correct comparison of the relative difference in prevalence between two population subgroups).
  
  
  CONCLUSIONS
  Profound population health messages can be lost through use of overly technical language and unfamiliar statistical measures. In our study, most participants did not understand age standardisation and confidence intervals. Inventive approaches are required to address this problem.
authors.affiliation: Centre for Epidemiology and Research, New South Wales Department of Health, 73 Miller Street, North Sydney NSW 2060, Australia. dmusc@doh.health.nsw.gov.au
authors.forename: David J
authors.initials: DJ
authors.lastname: Muscatello
authors.forename: Andrew
authors.initials: A
authors.lastname: Searles
authors.forename: Robin
authors.initials: R
authors.lastname: MacDonald
authors.forename: Louisa
authors.initials: L
authors.lastname: Jorm
mesh_terms: D000328:Adult; D001296:Audiovisual Aids / Q000592:standards*; D003142:Communication; D032882:Comprehension; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011634:Public Health