Coding for Journalists 104: Pfizer’s Doctor Payments; Making a Better List

Update (12/30): So about an eon later, I’ve updated this by writing a guide for ProPublica. Heed that one. This one will remain in its obsolete state.

Update (4/28): Replaced the code and result files. Still haven’t written out a thorough explainer of what’s going on here.

Update (4/19): After revisiting this script, I see that it fails to capture some of the payments to doctors associated with entities. I’m going to rework this script and post and update soon.

So the world’s largest drug maker, Pfizer, decided to tell everyone which doctors they’ve been giving money to to speak and consult on its behalf in the latter half of 2009. These doctors are the same ones who, from time to time, recommend the use of Pfizer products.

From the NYT:

Pfizer, the world’s largest drug maker, said Wednesday that it paid about $20 million to 4,500 doctors and other medical professionals for consulting and speaking on its behalf in the last six months of 2009, its first public accounting of payments to the people who decide which drugs to recommend. Pfizer also paid $15.3 million to 250 academic medical centers and other research groups for clinical trials in the same period.

A spokeswoman for Pfizer, Kristen E. Neese, said most of the disclosures were required by an integrity agreement that the company signed in August to settle a federal investigation into the illegal promotion of drugs for off-label uses.

So, not an entirely altruistic release of information. But it’s out there nonetheless. You can view their list here. Jump to my results here

Not bad at first glance. However, on further examination, it’s clear that the list is nearly useless unless you intend to click through all 480 pages manually, or, if you have a doctor in mind and you only care about that one doctor’s relationship. As a journalist, you probably have other questions. Such as:

  • Which doctor received the most?
  • What was the largest kind of expenditure?
  • Were there any unusually large single-item payments?

None of these questions are answerable unless you have the list in a spreadsheet. As I mentioned in earlier lessons…there are cases when the information is freely available, but the provider hasn’t made it easy to analyze. Technically, they are fulfilling their requirement to be “transparent.”

I’ll give them the benefit of the doubt that they truly want this list to be as accessible and visible as possible…I tried emailing them to ask for the list as a single spreadsheet, but the email function was broken. So, let’s just write some code to save them some work and to get our answers a little quicker.

This is part of a four-part series on web-scraping for journalists. As of Apr. 5, 2010, it was published a bit incomplete because I wanted to post a timely solution to the recent Pfizer doctor payments list release, but the code at the bottom of each tutorial should execute properly. The code examples are meant for reference and I make no claims to the accuracy of the results. Contact if you have any questions, or leave a comment below.

DISCLAIMER: The code, data files, and results are meant for reference and example only. You use it at your own risk.

The Code

The following code uses the same nokogiri strategies in the past three lessons. But here are the specific considerations that we have to make for Pfizer’s list:

  • The base url is: The most interesting parameter, iPageNo, is bolded. If you replace ‘1’ with any number, you’ll see you can progress through the list. There appears to be 486 pages.
  • So each page has a table of data with id #hcpPayments. The rows of data aren’t very normalized. For example, each “Entity Paid” can have many services/activity listed, with each of those items having another name attached to it. Then there are “cash” and “non-cash” values, which may or may not be numeric (“—” apparently means 0) There’s no easy css selector to grab each entity…but it seems that we can safely assume that if the first table column has a name (and the second and third contain city and state) that this is a new entity.
  • These are the steps we’ll take:

    • Download pages 1 to 486 of the list (each page has 10 entries)
    • Run a method that gathers all the doctor names from the pages we just downloaded on to our hard drive)
    • From that list of doctors, query the Pfizer site and gather the individual payments to every doctor.

    At the top, I’ve written a few convenience methods to deal with strings. Also included are: get_doc_query is a function we call to extract the doctor name from the links on the site.

    puts_error is a quick function to log any errors we might have

    						# Some general functions to deal with strings
    					class String
    					  alias_method :old_strip, :strip
    					  def strip
    						  self.old_strip.gsub(/^[\302\240|\s]*|[\302\240|\s]*$/, '').gsub(/[\r\n]/, " ")
    					  def strip_for_num
    					    self.strip.gsub(/[^0-9]/, '')
    					  def blank?
    						respond_to?(:empty?) ? empty? : !self
    					def get_doc_query(str)
    					def puts_error(str)
    					  err = "#{}: #{str}"
    					  puts err
 "pfizer_error_log.txt", 'a+'){|f| f.puts(err)}

    I found it easiest to download all the pages onto the hard drive first, using something like CURL, and then run the following code on it.

    process_local_pages is a method that will iterate through every page (you can set BASE_URL to either your hard drive if you’ve downloaded all the pages yourself, or to the Pfizer page), run process_row, and store all the doctor names and payees into separate files, as well as hold all the total amounts

    The three resulting files that you get are:

    • pfizer_doctors.txt – Every doctor name listed. We will use this in the next step to query each doctor individual on Pfizer’s site
    • pfizer_entities.txt – A list of every payment made to Entities
    • pfizer_entity_totals.txt – A list of the total payments made to Entities
    						def process_row(row, i, current_entity, arrays)  
    						  tds = row.css('td').collect{|r| r.text.strip}
    						   if !tds[3].blank? 
    						     if !tds[1].blank?
    						     # new entity
    						     puts tds[0]
    							     current_entity = {:name=>tds[0],:city=>tds[1], :state=>tds[2], :page=>i, :services=>[]} 
    							     arrays[:entities].push(current_entity) if arrays[:entities]
    						  	   current_class = row['class']
    						     if tds[3].match(/Total/)
    						       arrays[:totals].push([current_entity[:name], tds[4].strip_for_num, tds[5].strip_for_num].join("\t")) if arrays[:totals]
    						        # new service
    						   	   services_td = row.css('td')[3]
    						   	   service_name = services_td.css("ul li a")[0].text.strip 
    						   	   puts "#{current_entity[:name]}\t#{service_name}" 
    						   	   current_entity[:services].push([service_name, tds[4].strip_for_num, tds[5].strip_for_num]) 
    						   	   arrays[:doctors].push(services_td.css("ul li ul li a").map{|a| get_doc_query(a['href']) }.uniq) if arrays[:doctors]
    						   elsif tds.reject{|t| t.blank?}.length == 0
    						     #blank row
    						     puts_error "Page #{i}: Encountered a row and didn't know what to do with it: #{tds.join("\t")}"
    						   return current_entity
    						def process_local_pages
    						  doctors_arr = []
    						  entities_arr = []
    						  totals_arr =[]
    						  for i in 1..END_PAGE
    						  	   page = Nokogiri::HTML(open("#{BASE_URL}#{i}.html"))
    						    	 count1, count2 = page.css('#pagination td.alignRight').last.text.match(/([0-9]{1,}) - ([0-9]{1,})/)[1..2].map{|c| c.to_i}
    						    	 count = count2-count1+1
    						    	 puts_error("Page #{i} WARNING: Pagination count is bad") if count < 0
    						    	 puts("Page #{i}: #{count1} to #{count2}")
    						    	 rows = page.css('#hcpPayments tbody tr')
    						    	 rows.each do |row|  	   
    						    	   current_entity= process_row(row, i, current_entity, {:doctors=>doctors_arr, :entities=>entities_arr, :totals=>totals_arr})
    						     rescue Exception=>e
    						  	   puts_error "Oops, had a problem getting the #{i}-page: #{[e.to_str,{|b| "\n\t#{b}"}].join("\n")}"
  "pfizer_doctors.txt", 'w'){|f|
    						    doctors_arr.uniq.each do |d|
  "pfizer_entities.txt", 'w'){|f|
    						    entities_arr.each do |e|
    						      e[:services].each do |s|
  "pfizer_entity_totals.txt", 'w'){|f|
    						    totals_arr.uniq.each do |d|

    process_doctor is what we run after we’ve compiled the list of doctor names that show up on the Pfizer list. Each doctor has his/her own page with detailed spending. The data rows are roughly in the same format as the main list, so we reuse process_row again


    						def process_doctor(r, time='')
    						    url = "#{DOC_QUERY_URL}#{r}"
    						    page = Nokogiri::HTML(open("#{url}"))
    							   puts_error "Oops, had a problem getting the #{r}-entry: #{[e.to_str,{|b| "\n\t#{b}"}].join("\n")}"
    						  rows = page.css('#hcpPayments tbody tr')
    						  entities_arr = []
    						   rows.each do |row|  	   
    						     current_entity= process_row(row, '', current_entity, {:entities=>entities_arr})
    						   name = r.split('+')
    						   puts_error("Should've been a last name at #{r}") if !name[0].match(/,$/)
    						   name = "#{name[0].gsub(/,$/, '')}\t#{name[1..-1].join(' ')}"
    						   entities_arr.each do |e| 
    						     e[:services].each do |s|
    						  vals.each{|val|"pfizer_doctor_details.txt", "a"){ |f| 
    						    f.puts val
    						  puts vals
    						  return vals

    process_doctor_pages is just a function that calls process_doctor for each name in the pfizer_doctors.txt we previously gathered

    The final result is pfizer_doctor_details.txt, which contains a line for every payment to every doctor.

    						def process_doctor_pages
    						  time =
  "pfizer_doctors.txt", 'r'){|f|
    						     f.readlines.each do |r|
    						        vals = process_doctor(r, time)

The Results

After Googling the top-Pfizer-paid-doctor on the list (Gerald Michael Sacks for ~$150K), I came across the Pharma Conduct blog, which had already posted partial aggregations of the list, including the top 5 doctors, complete with profiles and pics.

As Pharma Conduct has already been on the ball, I’ll defer to its analysis. It has some good background here on how lame pharma companies have been in past releases of data. Overall, Pharma Conduct is less-than impressed with Pfizer:

Despite reporting more information than some its peers, Pfizer’s interface is still very limited. For one, to use the search filtering, you must know a physician’s first name and last name, as well as the state where the payment was made. Also, the data cannot be sorted by payment amount, which is a big limitation. Pfizer should be given credit for releasing the information and being so thorough. However, by releasing it in a format that is not really amenable to data analysis and is more suited to simply looking up results one physician at a time, I echo John Mack’s sentiment, namely, that this data is translucent, but not transparent.

I'm a programmer journalist, currently teaching computational journalism at Stanford University. I'm trying to do my new blogging at