PubMed publications output of world countries in 2011

PubMed publications in 2011 by 202 world countries: who’s the winner?

Introduction

I had this idea that it’d be fun to look at all PubMed’s articles from 2011 and extract country affiliation for each individual country. So I set out to do just that, but in addition to just look at 2011 I also looked at proportional change in publication 1980–2010 for the top 20 countries. The data for 2011 is visualized on a world map both as a bubble plot and as a heat map.

It turned that this project weren’t as straightforward as I first had anticipated. Mainly because PubMed’s affiliation field is a veritable mess with no apparent reporting standard. I imagine there are databases who are much more suited to this task than PubMed.

Method

There were 986 427 articles published in PubMed in 2011; so I, naturally, used R to extract national publication counts. I did this by downloading all citations into one 8.37 Gb XML-file, imported the affiliation strings into MySQL and then used R to extract country affiliation using grep and regular expressions.

To avoid unnecessary manual work I used lists of country names, U.S state & university names, India states and Japan universities. I also looked at word frequencies for the affiliations strings that couldn’t be matched, and used this to make additional pattern lists. Lastly, I also used mail-suffixes to extract affiliation.

Reliability

To find out how many mismatches my script perfomed, I drew a random sample (n = 2000) and manually screened for errors. 22 errors were found, and all of them entailed the string being matched to the correct country plus one incorrect country, i.e. this string were matched to both UK and US (because “Bristol” is matched to UK):

Department of Biotransformation, Bristol-Myers Squibb, Route 206 and Province Line Road, Princeton, NJ 08543, USA. anthony.barros@bms.com

It’s not really a big problem since it only occurs in 1.1 % of the sample. The following countries had erroneous extra matches in my random screening sample:

            x  freq
2    Australia    1
3      Austria    1
4        China    3
5       France    1
6        India    2
7        Japan    1
8         Oman    1
9  Saint Lucia    2
10          UK    8
11         USA    2

Moreover 1.8% of the affiliation strings couldn’t be matched to any country, by analyzing the word frequencies for the unmatched strings, I concluded there didn’t appear to be any words that could be used to identify an significant amount of countries.

Additionally, I compared the number of hits for my top 20 countries to the corresponding hits when searching PubMed using rudimentary country queries. These were the results:

                                                                   search      R PubMed   dif error
1  United States of America[ad] OR United States[ad] OR US[ad] OR USA[ad] 252796 242050 10746  0.04
2                                                               China[ad]  77614  76359  1255  0.02
3                             UK[ad] OR United Kingdom[ad] OR England[ad]  56069  54661  1408  0.03
4                                                               Japan[ad]  51740  48518  3222  0.06
5                                          Germany[ad] OR Deutschland[ad]  48183  44405  3778  0.08
6                                                              Canada[ad]  31926  29386  2540  0.08
7                                                               Italy[ad]  31883  30971   912  0.03
8                                                              France[ad]  31233  28832  2401  0.08
9                                                               Spain[ad]  24901  21268  3633  0.15
10                                                          Australia[ad]  23807  22891   916  0.04
11                                                              Korea[ad]  23796  23778    18  0.00
12                                                              India[ad]  23371  23093   278  0.01
13                                                        Netherlands[ad]  20002  19602   400  0.02
14                                               Brazil[ad] OR Brasil[ad]  18868  18223   645  0.03
15                                                             Taiwan[ad]  12324  12321     3  0.00
16                                                        Switzerland[ad]  11685  10320  1365  0.12
17                                                             Sweden[ad]  11018  10506   512  0.05
18                                                            Belgium[ad]   8551   8146   405  0.05
19                                                             Poland[ad]   7914   6526  1388  0.18

The measurement error is a bit high in countries like Poland, Switzerland and Spain. Nonetheless, I decided to use these PubMed quires to look at annual publications for these countries 1980–2010, using my PubMed trend script

Results

In total 202 countries were extracted, with the publication distribution looking like this:
PubMed publications world map plot
PubMed publications top 20 countries world map bubble plot
The same plot as above, but with the bubble size representing publications per capita.
PubMed publications per capita top 20 countries world map bubble plot

And a plot of the top 20 countries publication percentages 1980–2010

PubMed publications top 20 country year 1980–2010

I really don’t know why USA had such a boost in the 1990s, perhaps it got something to do with PubMed’s indexing or maybe it’s a consequence of the “1990s United States boom”? The reason for the sudden increase in US citations in the 90s is that prior to 1995 MEDLINE did only record institution, city, and state including zip code for authors affiliated with the US. So naturally, my queries will miss most US publications prior to 1995. However, the apparent question is: when will china surpass US in scientific output?

PS 1. Thanks to Allan Just for telling me how to extract centroid values from the country polygons.

PS 2. My plan is to do some more in-depth analyzes if this data, e.g. to look at publications per capita (in a vain attempt to increase Sweden’s rankings) and some traditional statistical analysis. Update: Publications per capita added.

Kristoffer Magnusson

I'm a PhD-student and a clinical psychologist from Sweden with a passion for research and statistics. This is my personal blog about psychological research and statistical programming with R.

Comments (14) Write a comment

  1. Kristoffer,

    I like your map. Can you please share the R code for the map.

    Thanks,
    Gary Guan

    Reply

    • Hi Gary,

      I believe I will have it up by the end of this week. But just so you know, the figures are not entirely made in R; they have been polished in Illustrator.

      Reply

  2. H Kristoffer,

    Nice work. About the mismatches of the script performed, in all cases — “your R script” identified higher than “PubMed using rudimentary country queries”.

    I was wondering if it was really an error !!!

    You have extracted the data from “PubMed’s affiliation field” — where there will be multiple countries for multi-country-author papers. But “PubMed using rudimentary country queries” is based on (probably) corresponding author’s country.

    This may be the reason of the miss-match !!!

    Regards

    Turin

    Reply

    • Hi Turin,

      The reason that my script retrieves more hits is that it’s using many, many more quires to match affiliations to countries than my PubMed search.

      For instance my script retrieves 3222 more hits for Japan than my PubMed.com search. This is because it’s also searching for large Japanese cities and popular Japanese universities. Searching for japan[ad] will miss fields where only the town or university is reported but not the country name.

      Thanks for your comment!

      Reply

    • Hi Vladimir,

      I believe I will have it up by the end of this week. But just so you know, the figures are not entirely made in R; they have been polished in Illustrator.

      Reply

  3. Great job! But I suspect you mixed up labels for Netherlands and Belgium.

    Reply

    • Darn, you are correct! Thanks for pointing it out, it’s been corrected now.

      Reply

  4. Wow, great. Can you explain a little more about how you used mysql to store and access the 8gig? Or point me to any helpful tutorial or reference. Thanks, Felix

    Reply

  5. Kristoffer, amazing work! could you share your entire code, including your regular expressions?

    Reply

  6. Thanks for another great blog post! It is very instructive to see your code :-)

    Reply

    • Thank you! It would be interesting to see Sweden’s change over time, perhaps I will look in to that in the future.

      Reply

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