The Statistics of Counterfeit/Fake Drugs, 2: analysis of the US-FDA FAERS data from 2013 to 2016

Study Aims and Interpretation

A previous analysis suggested that in the US-FDA FAERS drug-related adverse event database, the number of cases for oral and injectable drugs decreased from year 2015 to year 2016 whereas the number of cases with inhalational and topical dugs slightly increased. In addition, the number of cases reported by physicians was found increasing. http://www.rpubs.com/vesicare/261737
Aims of this analysis were to describe drug names that were counterfieted or suspected counterfieted by country where incident was recorded. Results suggested that affected countries included not only USA but also those in Latin America, North Africa, Europe, Russia and Asia. Therefore, although this database would not have collected incidents worldwide, the US-FDA FAERS may serve as a useful tool to grasp a worldwide trend in the Substandard, Spurious, Falsely labelled, Falsified and Counterfeit (SSFFC) Medical Products.

Data Source

Files in the csv format were used. https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm082193.htm

Read files and Collect Case-ID/caseid for Counterfeit Incident

setwd("/Volumes/LaCie/FAERS/")
directories <- dir()
cl <- makeCluster(3)
registerDoParallel(cl)
caseid_fake <- foreach(i = 1: length(directories), .combine = c) %dopar% {
  setwd(paste0("/Volumes/LaCie/FAERS/", directories[i], "/ascii"))
  files <- grep("txt", dir(), value = T)
  files <- grep("^REAC", files, value = TRUE)
  data<-read.csv(files, header = TRUE, sep = "$")
  data[grep("counterfeit", data$pt, ignore.case = TRUE), "caseid"]
}
stopCluster(cl)

Collect Record of Counterfeit Incident with Drug Name of Primary Suspect and Route

setwd("/Volumes/LaCie/FAERS")
directories <- dir()
cl <- makeCluster(3)
registerDoParallel(cl)
drug <- foreach(i = 1: length(directories), .combine = rbind) %dopar% {
  setwd(paste0("/Volumes/LaCie/FAERS/", directories[i], "/ascii"))
  files <- grep("txt", dir(), value = T)
  files <- grep("^DRUG", files, value = TRUE)
  data<-read.csv(files, header = TRUE, sep = "$")
  data[data$caseid %in% caseid_fake & data$role_cod == "PS",
       c("caseid", "drugname", "route")]
}
drug[]<-parLapply(cl, drug, as.character)
stopCluster(cl)

Collect Date/fda_dt, Type of Reporter/occp_cod and Country/occur_country of Incident from Demographic data

setwd("/Volumes/LaCie/FAERS")
directories <- dir()
cl <- makeCluster(3)
registerDoParallel(cl)
date <- foreach(i = 1: length(directories), .combine = rbind) %dopar% {
  setwd(paste0("/Volumes/LaCie/FAERS/", directories[i], "/ascii"))
  files <- grep("txt", dir(), value = T)
  files <- grep("^DEMO", files, value = TRUE)
  data<-read.csv(files, header = TRUE, sep = "$")
  data[data$caseid %in% caseid_fake, c("caseid", "fda_dt", "occp_cod","occr_country")]
}
date[] <- parLapply(cl, date, as.character)
stopCluster(cl)

Merge the Record fo Counterfeit Incident and the Date in Demographic Data

data <- merge(drug, date, by="caseid")
data$drugname <- toupper(data$drugname) %>% str_extract('[A-Z]+')
data$route <- toupper(data$route)
data[] <- lapply(data, as.character)
data$route <- toupper(data$route) %>% str_replace_all("-", "") %>% str_extract("[A-Z]+")
data$route[is.na(data$route)] <- "UNKNOWN"
data$occp_cod[data$occp_cod ==""] <- "UNKNOWN"
data$occr_country[data$occr_country ==""] <- "UNKNOWN"

Results

Table 1. Drug Name reported by Physician

The table shows drug names that appeared thrice or more.

data %>% filter(occp_cod=="MD") %>% group_by(drugname, occr_country) %>% summarise(Number_Report = n()) %>% arrange(desc(Number_Report)) %>% filter(Number_Report >= 3) %>% kable(format="markdown")

drugname	occr_country	Number_Report
AVASTIN	MX	37
HYDROCODONE	US	18
OXYCONTIN	US	17
VIAGRA	US	9
PREMARIN	US	8
COMBIGAN	EG	7
ALPRAZOLAM	US	6
AVASTIN	US	5
AVASTIN	IN	4
FENTANYL	US	4
HALOPERIDOL	CH	4
MIRAPEX	KR	4
TYZINE	RU	4
ZYPREXA	BG	4
ACEON	RU	3
CIALIS	US	3
NEXAVAR	RO	3
ONICIT	MX	3

Table 2. Drug Name reported by Pharmacist

The table shows drug names that appeared twice or more.

data %>% filter(occp_cod=="PH") %>% group_by(drugname, occr_country) %>% summarise(Number_Report = n()) %>% arrange(desc(Number_Report)) %>% filter(Number_Report >= 2) %>% kable(format="markdown")

drugname	occr_country	Number_Report
ONICIT	MX	7
VIAGRA	US	6
INVEGA	US	4
CIALIS	US	3
ALPRAZOLAM	US	2
CIALIS	JP	2
CRESTOR	US	2
HYDROCODONE	US	2
JANUVIA	US	2
OXYCODONE	US	2
PROPECIA	US	2

Table 3. Drug Name reported by Other Professional

The table shows drug names that occured twice or more.

data %>% filter(occp_cod=="OT") %>% group_by(drugname, occr_country) %>% summarise(Number_Report = n()) %>% arrange(desc(Number_Report)) %>% filter(Number_Report >= 2) %>% kable(format="markdown")

drugname	occr_country	Number_Report
OXYCONTIN	US	6
ATRIPLA	US	4
ONICIT	MX	4
VIAGRA	US	4
CYMBALTA	US	2
ESTRING	US	2
HERCEPTIN	MX	2
PREMARIN	US	2

Table 4. Drug Name reported by Consumer

The table shows drug names that appeared 10-times or more.

data %>% filter(occp_cod=="CN") %>% group_by(drugname, occr_country) %>% summarise(Number_Report = n()) %>% arrange(desc(Number_Report)) %>% filter(Number_Report >= 10) %>% kable(format="markdown")

drugname	occr_country	Number_Report
VIAGRA	US	186
CIALIS	US	127
DEPAKINE	CN	34
CYMBALTA	US	27
PLAVIX	CN	24
HEPTODIN	CN	20
PREMARIN	US	18
GLUCOBAY	CN	17
ALPRAZOLAM	US	15
FENTANYL	US	12
SERETIDE	CN	12
CELEBREX	US	11
PREMPRO	US	10
SILDENAFIL	US	10

Table 5. Drug Name reported by Unknown