Analysis
First, payment data were descriptively analyzed. Payments per physician were also calculated only for physicians receiving payment each year, as in other previous studies.[7, 12, 14, 32] Second, the payment concentration was evaluated by the shares of the payment values held by the top 1%, 5%, 10%, and 25% of the otorhinolaryngologists and the Gini coefficient at the physician level. The Gini index ranges from 0 to1, and the greater the Gini index, the greater the disparity in the distribution of payments.[4, 7, 33] Third, we calculated descriptive statistics and evaluated payment differences among the leading otorhinolaryngologists, including guideline authors, society board members, journal editors, and other otorhinolaryngologists. The differences in payments by each variable were evaluated by Chi-square and fisher exact tests for the proportion of otorhinolaryngologists receiving payments and by Mann-Whitney U test for payment values per otorhinolaryngologist. Furthermore, the linear log-linked Poisson regression model was used to assess the association between relative risk of payment receipt and the otorhinolaryngologist characteristics. To account for the skewed distribution of payment values, negative binomial regression model was employed to evaluate the association between relative monetary value of payments per physician and the otorhinolaryngologist characteristics. Finally, we evaluated the trends in payments per physician and number of physicians receiving payments between 2016 and 2019 by the population-averaged generalized estimating equation (GEE) with the panel data of the annual payments. As the payment distribution was highly skewed (Supplemental Material 1), the negative binomial GEE model for the payment values per physician and linear log-linked GEE model with Poisson distribution for the number of otorhinolaryngologists with payments were selected.[7, 34] The payment values were converted from Japanese yen (¥) to US dollars ($) using the 2019 average monthly exchange rates of ¥109.0 per $1. All analyses were conducted using Microsoft Excel, version 16.0 (Microsoft Corp) and Stata version 15 (StataCorp).