Table 2

Changes in awareness and knowledge of industry payments after payments information disclosure

Mean or percentageChangeDifference-in-difference estimatesP value†
2014 (%)2016 (%)2014–16 (%)Unadjusted difference in change (%)Regression-adjusted difference in change (95% CI)*
Awareness and knowledge of industry payments (% Answering Yes)
 Aware of industry payments (2014 mean 46.0, SE 1.3)
  Non-Sunshine states45.554.18.73.12.3% (−4.0% to 8.6%)0.470
  Sunshine states58.063.65.6
 Aware that industry payments info publicly available (2014 mean 10.2, SE 0.7)
  Non-Sunshine states9.812.93.29.99.6% (2.3% to 16.9%)0.011‡
  Sunshine states19.412.6−6.7
 Know whether own doctor has received industry payments (2014 mean 4.4, SE 0.6)
  Non-Sunshine states4.43.1−1.3−0.2−0.1% (−2.3% to 2.0%)0.918
  Sunshine states3.82.7−1.1
  • Analyses of awareness and knowledge measures based on balanced panel of individuals with non-missing survey items who responded to both 2014 and 2016 surveys: 1831 non-Sunshine residents and 197 Sunshine residents for awareness of payments; 1834 non-Sunshine residents and 196 Sunshine residents for awareness that payments information was public and for knowledge of whether own doctor had received payments.

  • *Regression models include age, education categories, urban residence, household income categories, employment categories, previous diagnosis of chronic conditions (which include acid reflux, asthma, atrial fibrillation, COPD, chronic pain, cystic fibrosis, diabetes, epilepsy, eye disease, gout, heart disease, hepatitis C, hypertension, high cholesterol, HIV, kidney disease, multiple sclerosis, osteoarthritis, osteoporosis, rheumatoid arthritis and sleep disorder), previous diagnosis of cancer, previous diagnosis of stroke or myocardial infarction, previous diagnosis of mental health disorder, number of visits to the doctor, whether insured, quadratic terms of age and number of visits to account for non-linearities in age and visits, year fixed effects and individual fixed effects (which absorb gender, race/ethnicity and other time-invariant individual characteristics). All analyses used Gfk-constructed weights that adjusted for non-coverage, non-response, oversampling and attrition. Standard errors were clustered at the state level.

  • †Reported p values for regression-adjusted change.

  • ‡Significant at 0.05 level.