##Compare payment of 2014 to 2020

The payment amount significantly increased starting in 2014, with the median payment value almost doubling by 2020 (32[115] vs. 62[482] ; p<0) (Chart 1)

##Plot the data ##The plot needs to be improved to get rid of the effect of the outlier

##Plot nature of payment

When the nature of the individual non-research payments was examined, royalty or license received the largest median dollar amount (20,263 [IQR 16,584; p< 0, when correcting for multiple comparisons using Bonferroni) compared to the other types of payments, including non continuing education programs, consulting fee, honoraria, faculty compensation, charitable contribution, and travel and lodging respectively (Chart 2).

However, when examining the number of payments, over 66.0% of all the payments (Food and Beverage8298 non-research payments) investigated during this period were provided for food and beverage.

##non research payment by specialty

##table3 export

The median non-research payments among fellowship directors from all subspecialties in the timeframe examined was 41 [IQR 127]; there was a statistically significant difference in the mean payments by subspecialty (p<0). The fellowship directors that received the highest total sum of payment was female pelvic medicine and reconstructive surgery (obgyn). Gynecologic oncology physicians received the highest median amount of dollars per payment and the most number of payments (Table 3*).

Male fellowship directors received over two times more funding in non-research amounts compared to their female counterparts (63 [IQR 197] vs. 28 [IQR 99]; p = 0).

##payment per sex

When evaluating the drug or device associated with the highest median non-research payments, three of the four highest median payment amounts were received by gynecologic oncology fellowship directors (re-do table 3).

##Payment by the manufacturer

Information regarding the manufacturer of each drug or device associated with each payment was provided. When compared to other listed manufacturers, Kedrion Biopharma, Inc.

##Compare Koate to other drugs by payment

was associated with the highest mean payment amount for Koate (5000 [5000;5000]21.3 [13.2;120]0.088). However when payments were analyzed by the drug or device with which they were associated, independent from the manufacturer, no statistical significance was found.

Royalty payment for female pelvic medicine and reconstructive surgery fellowship directors

Female Pelvic Medicine and Reconstructive Surgery

Over fifty-nine percent of the non-research dollars in this time period were provided to female pelvic medicine and reconstructive surgery (obgyn) fellowship directors. The median payment amount of OBGYN trained FPMRS fellowship directors was about half that of urology trained fellowship directors (27 [IQR 101] vs. 54 [IQR 134]; p=0).

This data set distribution was skewed. Seventeen of the highest 20 payments went to one obstetrics and gynecology trained FPMRS fellowship director, with a sum of 4941757.18 in the form of Royalty or License.

The nature of payments for the highest median payment amount differed by whether FPMRS fellowship directors were OBGYN or Urology trained. OBGYN trained fellowship directors receiving their highest median payments from Royalty or License (20,263 [IQR 16,584) compared to urology trained FPMRS fellowship directors whose highest median payments was from Honoraria, faculty compensation (1,800 [IQR 1,297]; p=0).

Male FPMRS fellowship directors received a median payment amount more than twice (3 times) that of their female counterparts, regardless of their residency training (58) [IQR 163] vs 23 [IQR 81] , p=0).

##Compare FPMR payment per age bgroups

However, the age of fellowship directors that received the highest median payment amount differed by residency training within this group, with thirty-five to forty year old OBGYN trained fellowship directors and sixty to sixty-five year old urology trained fellowship directors receiving higher payments (60.92 [IQR 130.65] and 134.39, respectively; p<0.01). The median payment also differed within this cohort based on how long they had acted as fellowship director, with OBGYN fellowship directors who received the highest median payment serving as fellowship director for zero to four years ($36.67 [IQR $162.39]; p<0.01) and urology trained fellowship directors who served as fellowship director for nine to fourteen years ($87.59 [IQR $247.60]; p<0.01).

Compare FPMR payment per drugs

The drugs associated with the highest median payments differed by residency training. Urology-trained FPMRS fellowship directors received the highest median payment amount for Solesta (149 [IQR 149, 149], p<???), while OBGYN-trained FPMRS fellowship directors’ highest median payment was for Gilenya (743 [IQR 126, 1,983]; p<????).

Gynecologic Oncology

Non-research payments to gynecologic oncology fellowship directors comprised about one-third?? (??%) of the total drug or manufacturer payments evaluated during this period.

##Payment by go followship

The median payment amount provided to GO fellowship directors was over twice?? the median payment amount provided to other fellowship directors ($??[IQR $??] vs. $?? [IQR $??]; p<??).

##payment by nonCME

The highest median payment amount for this subspecialty was funding for ???? compared to the other payment types ($???? [IQR $???]; p<????).

payment by gender GYN Onc

There was ??? statistical significance in the median payment amount provided to GO fellowship directors based on gender (p=???).

##payment by age group

Within this subspecialty, GO fellowship directors who received higher median dollar amounts were between 35- to 40-years-old practicing in ACOG District IX (California) (p<0.01 for both).

Maternal-Fetal Medicine

One-third of fellowship directors who received drug or device manufacturer payments were maternal-fetal medicine fellowship directors. However, MFM fellowship directors only accounted for 8.5% of the total number of payments with a median income of $31 [IQR $144].

##payment per nature

MFM fellowship directors received the highest median payment amount for ???honoraria and faculty??? compensation when compared to other types of payments ($2,000 [IQR $2,773]; p<0.01).

##payment by sex

##payment by district

Those who generally received higher median payments among MFM fellowship directors were ??males between ??fifty-one and fifty-five?? years old, practicing in ACOG District ??? (p<??? for all).

##payment by drug

When comparing payment amounts by the manufacturer associated with each payment, the highest median payment amount to MFM fellowship directors was associated with ??? for the Hemophilia A medication Koate (anti-hemophilic factor), which was also associated with the highest overall median payment when compared to all payments received by MFM fellowship directors ($5,000 vs. $40.62 [IQR $190], p<0.01)

Reproductive Endocrinology and Infertility

Reproductive endocrinology and infertility fellowship directors accounted for 15% of fellowship directors who received non-research payments, receiving 6% of the total dollars provided to fellowship directors during the time frame investigated (median payment amount $28 [IQR $162]).

REI fellowship directors who received a higher median amount of money from drug or device manufacturers were ??males over age ??65, who resided in ACOG District ? (p<0.??? for all).

The highest median payment amount provided to REI fellowship directors was provided in ???consulting fees (??? [IQR ??]; p<0.??).

##payment by drug

Dependencies:

  • R version 4.1.0 (2021-05-18)
    • tidyverse (v. 1.3.1)
    • Rcpp (v. 1.0.6)
    • furrr (v. 0.2.3)
    • data.table (v. 1.14.0)
    • here (v. 1.0.1)
    • rmarkdown (v. 2.9)

My computer

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] furrr_0.2.3         future_1.21.0       Rcpp_1.0.6         
##  [4] rmarkdown_2.9       data.table_1.14.0   broom_0.7.8        
##  [7] english_1.2-5       here_1.0.1          forcats_0.5.1      
## [10] stringr_1.4.0       purrr_0.3.4         readr_1.4.0        
## [13] tidyr_1.1.3         tibble_3.1.2        tidyverse_1.3.1    
## [16] dplyr_1.0.7         cowplot_1.1.1       compareGroups_4.5.1
## [19] ggpubr_0.4.0        ggplot2_3.3.5       knitr_1.33         
## 
## loaded via a namespace (and not attached):
##  [1] fs_1.5.0            lubridate_1.7.10    webshot_0.5.2      
##  [4] httr_1.4.2          ggsci_2.9           rprojroot_2.0.2    
##  [7] tools_4.1.0         backports_1.2.1     bslib_0.2.5.1      
## [10] utf8_1.2.1          R6_2.5.0            DBI_1.1.1          
## [13] colorspace_2.0-2    withr_2.4.2         tidyselect_1.1.1   
## [16] curl_4.3.2          compiler_4.1.0      chron_2.3-56       
## [19] cli_3.0.0           rvest_1.0.0         HardyWeinberg_1.7.2
## [22] flextable_0.6.6     mice_3.13.0         xml2_1.3.2         
## [25] officer_0.3.18      labeling_0.4.2      sass_0.4.0         
## [28] scales_1.1.1        systemfonts_1.0.2   digest_0.6.27      
## [31] foreign_0.8-81      svglite_2.0.0       rio_0.5.27         
## [34] base64enc_0.1-3     pkgconfig_2.0.3     htmltools_0.5.1.1  
## [37] parallelly_1.26.1   highr_0.9           dbplyr_2.1.1       
## [40] rlang_0.4.11        readxl_1.3.1        rstudioapi_0.13    
## [43] farver_2.1.0        jquerylib_0.1.4     generics_0.1.0     
## [46] jsonlite_1.7.2      zip_2.2.0           car_3.0-11         
## [49] magrittr_2.0.1      kableExtra_1.3.4    Matrix_1.3-4       
## [52] munsell_0.5.0       fansi_0.5.0         abind_1.4-5        
## [55] gdtools_0.2.3       lifecycle_1.0.0     stringi_1.6.2      
## [58] yaml_2.2.1          carData_3.0-4       grid_4.1.0         
## [61] listenv_0.8.0       parallel_4.1.0      crayon_1.4.1       
## [64] lattice_0.20-44     haven_2.4.1         splines_4.1.0      
## [67] hms_1.1.0           pillar_1.6.1        uuid_0.1-4         
## [70] ggsignif_0.6.2      codetools_0.2-18    reprex_2.0.0       
## [73] glue_1.4.1.9000     evaluate_0.14       modelr_0.1.8       
## [76] vctrs_0.3.8         cellranger_1.1.0    gtable_0.3.0       
## [79] assertthat_0.2.1    xfun_0.24           openxlsx_4.2.4     
## [82] Rsolnp_1.16         rstatix_0.7.0       survival_3.2-11    
## [85] viridisLite_0.4.0   truncnorm_1.0-8     writexl_1.4.0      
## [88] globals_0.14.0      ellipsis_0.3.2