special article mapping epidemiology's past to inform its future

Special Article

Mapping Epidemiology’s Past to Inform Its Future: Metaknowledge Analysis of

Epidemiologic Topics in Leading Journals, 1974–2013

Ludovic Trinquart* and Sandro Galea

* Correspondence to Dr. Ludovic Trinquart, Department of Epidemiology, Mailman School of Public Health, Columbia University,

722 West 168th Street, New York, NY 10032 (e-mail: [email protected]).

Initially submitted July 29, 2014; accepted for publication January 28, 2015.

An empiric perspective on what epidemiology has studied over time might inform discussions about future direc-

tions for the discipline. We aimed to identify the main areas of epidemiologic inquiry and determine how they evolved

over time in 5 high-impact epidemiologic journals. We analyzed the titles and abstracts of 20,895 articles that were

published between 1974 and 2013. In 5 time periods that reflected approximately equal numbers of articles, we iden-

tified the main topics by clustering terms based on co-occurrence. Infectious disease and cardiovascular disease ep-

idemiology were the prevailing topics over the 5 periods. Cancer epidemiology was a major topic from 1974 to 2001

but disappeared thereafter. Nutritional epidemiology gained relative importance from 1974 to 2013. Environmental

epidemiology appeared during 1996–2001 and continued to be important, whereas 2 clusters related tomethodology

and meta-analysis in genetics appeared during 2008–2013. Several areas of epidemiology, including injury or psy-

chiatric epidemiology, did not make an appearance as major topics at any time. In an ancillary analysis of 6 high-

impact general medicine journals, we found patterns of epidemiologic articles that were overall consistent with the

findings in epidemiologic journals. This metaknowledge investigation allowed identification of the dominant topics

in and conversely those that were absent from 5 major epidemiologic journals. We discuss implications for the field.

bibliometrics; knowledge; periodicals as topic; terminology as topic

The definition of epidemiology has not changed significantlysince it originated. A review of 70 epidemiology textbooks pub-lished between 1931 and 2014 shows that epidemiology hasconsistently been defined as the science of understanding thedistribution and determinants of population health to be ableto intervene to control or prevent disease (Web Table 1 andWeb Figure 1, available at http://aje.oxfordjournals.org/). How-ever, the scope of epidemiologic study and practice has ex-panded substantially over the past few decades. Between 1974and 2013, there was a nearly 6-fold increase in the use of theterm epidemiology in papers indexed by MEDLINE.

Motivated in part by changes in funding opportunities andin the scale of population-based studies, several recent com-ments have been concerned with potential future directionsfor epidemiology as a discipline (1–7). However, much ofthis soul-searching has been informed principally by expertopinion, with little evidence to guide our thinking. An em-piric perspective on the field’s evolution may be useful tohelp guide our collective thinking about future research direc-tions for the field (8, 9). A large-scale content analysis cantrack how areas of epidemiologic research evolve, with various

areas gaining or losing importance. Underrepresented researchpaths might be due to a lack of attention to important areas thatneed to be looked at in the future.

Metaknowledge investigations can complement the ongo-ing self-reflection in the field (10). Because they involveanalyzing large quantities of texts, metaknowledge investi-gations have the potential to allow the investigation of thedistribution and relative influence of topics over time (11).Considering that what we, as epidemiologists, write shouldreflect our vision of the discipline, such analysis may helpus shape the discipline. Therefore, we aimed here to providean empirical perspective on the field of epidemiology byidentifying the main topics in 5 major epidemiology journalsand assessing how they had evolved over the past 40 years.

METHODS

Selection of articles

We considered 5 high-impact epidemiology journals: theAmerican Journal of Epidemiology, the International Journal

93 Am J Epidemiol. 2015;182(2):93–104

American Journal of Epidemiology

© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of

Public Health. All rights reserved. For permissions, please e-mail: [email protected].

Vol. 182, No. 2

DOI: 10.1093/aje/kwv034

Advance Access publication:

May 14, 2015

Downloaded from https://academic.oup.com/aje/article-abstract/182/2/93/94673by gueston 11 April 2018

http://aje.oxfordjournals.org/lookup/suppl/doi:10.1093/aje/kwv034/-/DC1


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of Epidemiology, the Annals of Epidemiology, Epidemiology,and the European Journal of Epidemiology. Their impactfactors are among the highest for the category public, envi-ronmental, and occupational health of the Journal CitationReports, and these journals are widely considered the jour-nals of record (12).We retrieved from MEDLINE via PUBMED the records

of all indexed articles published in these 5 journals up to2013, without any restriction on article type but includingonly articles for which an abstract was available. The se-lected articles were categorized into 5 time periods, andwe aimed to have approximately equal numbers of articlesin each time period. Data were analyzed separately for eachperiod of time.

Linguistic processing

For each article, we extracted the title and abstract and thencombined them into a single string. We discarded the wordsused to denote the structure of the abstract. Grammatical tag-ging allowed us to identify the part of speech (e.g., noun, pro-noun, adjective, noun, or verb) and assign the lemma (itscanonical form) of each word of a string. For instance, “genes”and “gene” would be assigned the lemma “gene.”Moreover,we developed a thesaurus that allowed for merging of differ-ent spellings of the same word (“ischaemic” and “ischemic”)and for merging an abbreviation with theword or phrase itself(PTSD and “posttraumatic stress disorder”). Each string wasthen reduced to a set of noun phrases, that is, single nouns orsequences of adjectives plus nouns or nouns that belong to-gether (e.g., “cardiovascular disease” or “relative risk”). Inthe remainder, noun phrases were referred to as terms.Terms that occurred multiple times within a string were

counted only once, and we discarded the terms that occurredin fewer than 10 articles. The relevance of each term was esti-mated as the degree to which the occurrences of the term wereoriented towards 1 or more topics underlying the articles. For agiven term, it was measured as the Kullback-Leibler distancebetween the distribution of (second-order) co-occurrences be-tween that term and all other terms and the overall distributionof co-occurrences over all terms. We selected the top 60% ofthe terms with the highest relevance (13).

Mapping and clustering of terms

The selected terms were positioned on a 2-dimensionalco-occurrence plot and were grouped into clusters based onthe co-appearance of terms. A normalized co-occurrence fre-quency was derived for each pair of terms. The locations ofterms on the plot were determined by minimizing a weightedsum of the squared distances between all pairs of terms. Min-imization was achieved through stress majorization (14).Consequently, terms with high co-occurrence tend to be closeto each other, whereas terms that are far away from each otherdo not or rarely occur together in the same article (15). Theterms were also assigned to clusters using a weighted variantof modularity-based clustering (16, 17). We characterizedeach cluster by providing a heading based on the terms inthe cluster. We assessed the relative importance of clustersaccording to their share of terms relative to the total number

of terms. Clusters of terms are interpreted as major epidemi-ology topics, and clusters located close to each other in themap indicate related topics.For each time period, the resulting maps show terms as la-

beled nodes in the co-occurrence network. Node size is pro-portional to the term frequency of occurrence, so that thelarger the node, the more articles include the term. The clus-tering of the terms is displayed on top of the map by coloringnodes based on the cluster to which they belong. Analysis in-volved the use of the VOSviewer software, version 1.5.7(Centre for Science and Technology Studies, Leiden Univer-sity, The Netherlands) (18).

Identification of bursts

To identify topics that attracted attention in epidemiologyresearch but eventually faded away, we used Kleinberg’sburst detection algorithm to identify words that experiencedsudden increases in use (19, 20). The algorithm assessesstates of the document stream, with different frequencies ofindividual words, and identifies state transitions, that is,years around which the frequency of a word’s usage changessignificantly. The analysis generates a list of burst words, to-gether with the intervals of time during which each burst oc-curred and the intensity of the burst. We visualized the top100 burst words graphically on a horizontal bar chart, withpublication year on the x-axis, burst words on the y-axis,and a bar from the start to the end of the burst. The barwidth is proportional to the intensity of the burst. Barswere color-coded according to the major epidemiology top-ics, as previously. Some words did not belong to any partic-ular cluster, and the corresponding bars were left uncolored.Analysis involved the use of the Science of Science Tool,version 1.1 β (Cyberinfrastructure for Network Science Cen-ter, Indiana University, Bloomington, Indiana, http://sci2.cns.iu.edu).

Ancillary analysis of high-impact general medicine

journals

Because many epidemiologic articles are published out-side of epidemiology journals, we performed the followingancillary analysis. We considered 6 high-impact generalmedicine journals: The New England Journal of Medicine,The Lancet, The Journal of the American Medical Associa-tion, The BMJ, Annals of Internal Medicine, and PLoS Med-icine. To identify articles most likely of relevance to the fieldof epidemiology, we analyzed how the articles published inthe 5 epidemiology journals were indexed with MeSH termsin MEDLINE and we derived the following sensitivity-maximizing search filter: “epidemiology” (Subheading) OREpidemiologic Factors (MeSH) OR Epidemiologic Methods(MeSH) OR epidemiologic studies (MeSH). Using this filter,we retrieved from MEDLINE the records of articles with ab-stracts that were published in these 6 general medicine jour-nals during the same time period that the other articles in the 5epidemiology journals. The selected articles were catego-rized into the same 5 time periods. We applied the same lin-guistic processing to the titles and abstracts, and we mappedand clustered terms into major topics, as previously.

94 Trinquart and Galea

Am J Epidemiol. 2015;182(2):93–104


http://sci2.cns.iu.edu







RESULTS

Characteristics of selected articles

We selected 20,895 articles. Overall, 42.7% were publishedin the American Journal of Epidemiology, 21.7% in the Inter-national Journal of Epidemiology, 10.2% in the Annals ofEpidemiology, 10.9% in Epidemiology, and 14.5% in the Eu-ropean Journal of Epidemiology. Figure 1 shows the evolutionover time of the yearly number of articles across the 5 journals.In all, 3,725 (17.8%) articles were published between 1974and 1989; 3,948 (18.9%) were published between 1990 and1995; 4,492 (21.5%) were published between 1996 and 2001;4,180 (20.0%) were published between 2002 and 2007; and4,550 (21.8%) were published between 2008 and 2013.

Mapping and clustering of terms

Figure 2 shows the mapping and clustering of terms overtime. Table 1 shows a summary of the clusters of terms andthe evolution of major epidemiology topics. The map for1974–1989 contained 5 main clusters of co-occurring terms,which corresponded to infectious diseases epidemiology,cardiovascular diseases epidemiology, cancer epidemiol-ogy, reproductive and perinatal epidemiology, and nutritionepidemiology.

These 5 major topics were identified consistently over the1990–1995 and 1996–2001 periods. In addition, a clustercorresponding to female cancer epidemiology was identifiedfor 1990–1995 but disappeared thereafter, whereas a clusterrelated to environmental appeared during 1996–2001 andpersisted in the subsequent periods.

For the period of 2002–2007, infectious and cardiovascu-lar diseases epidemiology remained among the top majortopics. However, the cluster related to cancer epidemiologydisappeared, and the nutritional epidemiology cluster gaineda larger share of the term map. Moreover, a cluster related tomethodology appeared during 2002–2007, ahead of repro-ductive and perinatal epidemiology and environmental epi-demiology, and persisted in the subsequent period.

Finally, 2008–2013 saw the appearance of another clusterrelated to meta-analysis in genetics, and the period included atotal of 7 clusters. Cardiovascular diseases, nutrition, and in-fectious disease epidemiology remained the top major topics.Reproductive and perinatal epidemiology and environmentalepidemiology completed the picture.

Identification of bursts

The analysis of the 100 top burst words showed similarpatterns (Web Figure 2). From 1974 to 1999, all of the bursts

0

200

400

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800

1975 1980 1985 1990 1995 2000 2005 2010

Publication Year

No.

of A

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les

Journal

American Journal of EpidemiologyInternational Journal of EpidemiologyAnnals of Epidemiology

EpidemiologyEuropean Journal of Epidemiology

Figure 1. Number of articles published per year from 1974 to 2013 by journal.

Metaknowledge Analysis of Epidemiologic Topics 95

Am J Epidemiol. 2015;182(2):93–104



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Cancer

Infectious Disease



Nutritional

Environmental

Cancer

Infectious Disease



Nutritional

Female Cancer

Figure 2 continues


Am J Epidemiol. 2015;182(2):93–104


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Methodology

Infectious Disease

Nutritional

Environmental


Meta-analysisMethodology

Infectious Disease

Nutritional

Environmental



Figure 2. Mapping and clustering of terms in 5 high-impact epidemiology journals for A) 1974–1989, B) 1990–1995, C) 1996–2001, D) 2002–2007, and E) 2008–2013. The maps show terms as labeled nodes. Some terms appear to be misspelled or truncated because of the tasks of lin-guistic processing that were performed before themapping and clustering of terms, as described in theMethods section. Node size is proportional tothe term frequency of occurrence (i.e., the larger the node, the more articles include the term). Terms that are far away from each other do not orrarely occur together in the same article, whereas terms with high co-occurrence are close to each other. The clustering of the terms is displayed ontop of the map by coloring nodes based on the cluster to which they belong. Clusters of terms are interpreted as major epidemiology topics, andclusters located close to each other in the map indicate related topics.


Am J Epidemiol. 2015;182(2):93–104


Table 1. Evolution of Major Topics in 5 High-Impact Epidemiologic

Journals and in a Subset of Articles Published in 6 High-Impact

General Medicine Journals, 1974–2013

Topica

1974–1989

EpidemiologyJournals 3,725Articles, 951Terms, %

GeneralMedicine

Journalsb 8,602Articles, 823Terms, %

Infectious diseaseepidemiology

36 24

Infection 14 13

Antibody 8 6

Virus 7

Outbreak 6

Illness 6 6

Prevalence 5

United States 4

Cardiovascular diseaseepidemiology

24 16

Man 15

Smoking 7 3

Blood pressure 6

Cigarette smoking 5

Adjust 5

Risk factor 6

Relative risk 3

Diabetes 3

Cohort 2

Cancer epidemiology 15 19

Mortality 12

Cancer 11

Mortality rate 4

Death rate 3

Lung cancer 2

Therapy 14

Survival 5

Cell 4

Chemotherapy 3

Recipient 3

Reproductive andperinatal epidemiology

12 5

Case control study 12

Relative risk 7

Confidence interval 6

Pregnancy 4 4

Infant 4 5

Mother 3

Birth 2

Delivery 2

Clinical trials 20

Trial 10

Placebo 8

Table continues

Table 1. Continued

Topica

1974–1989

EpidemiologyJournals 3,725Articles, 951Terms, %

GeneralMedicine


Dose 7

Efficacy 6

Improvement 4

Health care quality 16

Physician 6

Care 4

Survey 4

Cost 3

Service 3

1990–1995

EpidemiologyJournals 3,948Articles, 1,116

Terms, %

GeneralMedicine



32 15

Infection 12 14

Antibody 5 5

HIV 5 7

Italy 4

Sensitivity 4

Detection 4

HIV infection 3


21 18

Body mass index 8

Cigarette smoking 6

Blood pressure 5

Coronary heart disease 5

Diabetes 5


Baseline 5

Adjust 5

Smoking 4

Hypertension 4

Cancer epidemiology 18

Cancer 10

Approach 5

Mortality rate 4

Validity 3

Example 3


14 8

Pregnancy 6 4

Mother 5 3

Birth 5 4

Table continues


Am J Epidemiol. 2015;182(2):93–104


Table 1. Continued

Topica

1990–1995


Terms, %

GeneralMedicine


Infant 5 5

Smoker 4

Screening 5

Nutritional epidemiology 8

Consumption 8

Diet 5

Alcohol 4

Correlation 4

Food 3

Female cancerepidemiology

8

Breast cancer 4

Parity 2

Family history 2

Oral contraceptive 2

Menopause 1


Care 9

Survey 8

Practice 6

Questionnaire 6

Physician 6

Clinical trials 21

Therapy 14

Trial 14

Placebo 9

Efficacy 9

Dose 8


12

Sensitivity 5

Myocardial infarction 4

Stroke 3

Specificity 3

Acute myocardial infarction 2

1996–2001


Terms, %

GeneralMedicine

Journalsb 6,891Articles, 1,009

Terms, %


36 25

Infection 11 13

Approach 5

HIV 4 6

Antibody 4

Table continues

Table 1. Continued

Topica

1996–2001


Terms, %

GeneralMedicine


Terms, %

Strategy 4

Survival 8

Cell 4

Sensitivity 4


21 29

Body mass index 10

Baseline 5

Physical activity 5

Hypertension 4 4

Alcohol consumption 4

Diabetes 5


Infant 4

Birth 4

Cancer epidemiology 15

Lung cancer 3

Cigarette 3

Nonsmoker 3

Cancer registry 2

Cancer risk 2


13

Pregnancy 7

Birth 7

Mother 5

Infant 5

Breast cancer 4


Consumption 7

Diet 4

Validity 3

Error 3

Agreement 2

Environmentalepidemiology

6

Asthma 2

Air pollution 2

Season 2

Respiratory symptom 1

Respiratory disease 1


Care 9

Quality 7

Survey 7

Practice 7

Table continues


Am J Epidemiol. 2015;182(2):93–104


Table 1. Continued

Topica

1996–2001


Terms, %

GeneralMedicine


Terms, %

Physician 7

Clinical trials 22

Trial 21

Placebo 11

Efficacy 10

Dose 7

Double blind 6

2002–2007


Terms, %

GeneralMedicine



Consumption 9

Diet 3

Inverse association 3

Incident case 2

Lower risk 2


22 20

Body mass index 10 4

Cardiovascular disease 6 5

Weight 5

Coronary heartdisease

5

Height 5

Stroke 5

Hypertension 5



20

Infection 8

Mortality rate 3

Transmission 3

HIV 3 4

Setting 2

Gene 4

Cell 4

Progression 3

Vaccine 3

Methodology 16

Approach 7

Epidemiology 5

Bias 5

Paper 5

Problem 4

Table continues

Table 1. Continued

Topica

2002–2007


Terms, %

GeneralMedicine



10

Pregnancy 7

Mother 6

Infant 4

Birth weight 4

Offspring 3


7

Asthma 2

Air pollution 2

Nonsmoker 2

Susceptibility 2

Season 2


Practice 7

Health 7

Survey 6

Research 6

Problem 5

Clinical trials 23

Placebo 12

Controlled trial 12

Hazard ratio 10

Efficacy 10

Dose 8

Meta-analysis 13

Quality 10

Review 6

Medline 6

Systematic review 6

Meta analysis 6

Cost-benefit analysis 2

Cost 5

Dollar 2

Cost effectiveness 2

Life year 2

Life expectancy 1

2008–2013


Terms, %

GeneralMedicine


Terms, %


23 18

Body mass index 12 5

Table continues


Am J Epidemiol. 2015;182(2):93–104


were related to infectious diseases except the words “systolic”and “diastolic,”which were related to the epidemiology of car-diovascular diseases. The word “seropositive” showed themost intense burst. After 2000, there was no clear time patternof burst. Some terms belonged to a single topic. For instance,

thewords “ambient” and “particulate”were exclusively relatedto environmental epidemiology. Eight terms were related togenetics and meta-analysis. Two of the terms with the most in-tense bursts (“gestational” and “preterm”) were related to re-productive and perinatal epidemiology. A majority of burst

Table 1. Continued

Topica

2008–2013


Terms, %

GeneralMedicine


Terms, %

Height 5

Childhood 3

Cause mortality 3

Blood pressure 3

Cardiovascular disease 5

Prospective cohort study 4

Smoking 4

Gene 3

Nutritionalepidemiology

20

Hazard ratio 10

Consumption 5

Diet 3

Health study 3

Smoker 3


19

Research 8

Epidemiology 6

Infection 6

Issue 4

Article 4

Methodology 15

Method 13

Approach 10

Bias 7

Problem 5

Design 5


11

Pregnancy 8

Mother 5

Childhood 4

Birth weight 3

Infant 3


7

Air pollution 3

Particulate matter 2

Table continues

Table 1. Continued

Topica

2008–2013


Terms, %

GeneralMedicine


Terms, %

Season 2

Temperature 1

Hospital admission 1

Meta-analysis 3 13

Meta analysis 5 9

Gene 4

Systematic review 3 8

Genotype 3

Polymorphism 2

Randomized controlledtrial

8

Medline 7

Database 7

Global health 38

Country 11

Prevalence 9

Trend 6

Survey 6

Cost 6

Clinical trials 20

Hazard ratio 15

Therapy 15

Week 13

Placebo 12

Clinical trial 11


13

Hazard ratio 15

Stroke 7

Significant difference 6

Myocardial infarction 6

Cause mortality 5

Abbreviation: HIV, human immunodeficiency virus.a Data are clusters of terms interpreted as major topics (with the

percentage of terms within each cluster) and the top 5 terms withineach cluster (with the percentage of articles including each term).The major topics are ordered by their importance in the epidemiologicjournals and then in the general medicine journals.

b For the 6 high-impact medical journals, we retrieved articles mostlikely of relevance to the field of epidemiology by using a customsearch filter.


Am J Epidemiol. 2015;182(2):93–104


wordswere related tomethodology (e.g., “pathway,” “Bayesian,”“causal,” “mediation,” and “simulation”). Finally, approxi-mately one-third of burst words did not belong to any par-ticular cluster, but most of them were related to methodology(e.g., “multilevel,” “P for trend,” “Cox,” “heterogeneity,”“modeling,” and “confounder”).

Ancillary analysis of high-impact general medicine

journals

Our search retrieved 32,760 articles most likely of rele-vance to the field of epidemiology that were published inthe 6 general medicine journals. Web Figure 3 shows themapping and clustering of terms over time. Table 1 shows asummary of the clusters of terms and allows comparison witharticles from epidemiologic journals.

Some topics were similar to those identified in epidemiol-ogy journals and showed similar evolution. Cardiovasculardiseases and infectious diseases were among the main topicsover the 5 time periods, except the last time period for infec-tious diseases. We identified a cancer cluster in the 1974–1989 period and a reproductive and perinatal health clusterover the 1974–1989 and 1990–1995 periods. A cluster re-lated to meta-analysis appeared during 2002–2007 and per-sisted in the subsequent period.

Other topics differed from those identified previously. Onecluster corresponding to clinical trial terms (in multiple clin-ical specialties) was identified over the 5 periods. Anothercluster related to health care quality was identified over theperiods from 1974 to 2007. Lastly, a cost-benefit analysiscluster was identified in 2002–2007, and a global health clus-ter was identified in 2008–2013.

DISCUSSION

We analyzed 20,895 articles published in 5 epidemiologyjournals over 4 decades using a production-oriented approachto investigate the epistemic core of epidemiology. We found aclear pattern of leading areas of epidemiologic inquiry duringthis period and patterns in the evolution of these areas. Wefound, first, that the epidemiology of infectious and cardiovas-cular diseases have consistently been the main topics of interestin these 5 journals. Second, cancer epidemiology has been amajor topic, with a peak in knowledge production in 1990–1995, where 2 clusters related to cancer and female cancerwere identified but stopped being a leading focus of papers inthe 5 epidemiologic journals after 2001. Third, nutritional epi-demiology gained importance over time. Fourth, 3 topics wereamong the leading areas of inquiry for time-delimited periods,namely environmental epidemiology since 1996, whereasmeth-odology and meta-analysis in genetics appeared in 2008–2013.

Becausewe focused our inquiry on these 5 leading epidemi-ology journals, we can interpret our findings as representingknowledge produced and regulated through peer review prin-cipally by epidemiologists and shaped by editorial processes inline with the leading epidemiologic organizations. The Amer-ican Journal of Epidemiology is published in association withthe Society for Epidemiologic Research, the InternationalJournal of Epidemiology is published on behalf of the Interna-tional Epidemiological Association, Epidemiology is the offi-

cial journal of The International Society for EnvironmentalEpidemiology, and the Annals of Epidemiology is the officialpublication of the American College of Epidemiology. By de-sign, this analysis, excludes papers published by epidemiolo-gists, or papers published by nonepidemiologists that wouldnonetheless be considered within the field’s remit, that werepublished in nonepidemiologic journals. There is little ques-tion that such papers thrive, particularly in clinical journals.So, for example, the decrease in focus on cancer in these 5journals over the past decade represents most likely a shift inwhere these papers are being published—away from epidemi-ology journals to cancer journals. We would argue, however,that there is consequence to publishing the relevant papersin the leading journals in the discipline. Epidemiologistsare, in many respects, the keepers of the methodological flamein population health sciences. If cancer epidemiology is evolv-ing in nonepidemiology journals, it represents a tremendouslost opportunity for the field to make the contribution it canand should make to one of the leading global causes of death.Therefore, although our observations are in some ways heart-ening, reinforcing that we are focusing on cardiovascular dis-ease commensurately with the contribution of cardiovasculardisease to burden of mortality, they also suggest that the disci-pline is playing a far smaller role in other areas that are alsoimportant. For example, although several new areas have gain-ed prominence in the field over the past decades, including so-cial epidemiology, these are clearly not represented among thekey areas in these 5 leading epidemiology journals over thetime period of interest (21–23).Moreover, areas such as injury,psychiatric, or neurological epidemiologyare clearly not amongthe main topics identified; this is dissonant with the importancethat these areas have for global burden of disease (24–26).Within a consequentialist epidemiology framework, it wouldcertainly stand the field in good stead if we engaged activelyaround inquiry concerning the major causes of morbidity andmortality worldwide, with an eye to how we may prevent dis-ease and improve health (27–29).

The increasing role that methodological papers play inpublication in epidemiology journals over the past decadepresents both opportunities and challenges. In some respects,this evolution represents an evolution in the field, whereinmethods for epidemiology are being developed principallyby epidemiologists. This reflects a maturity in the field, mov-ing well beyond its origins where methods in the disciplineemerged from other areas (8). However, it also suggests thatthe field takes upon itself greater responsibility, both to keepdeveloping methods that are adequate to the evolving popu-lation health challenges we face and to ensure flexibility tothe incorporation of methods that do arise in other areas thatmay be fruitful for epidemiology to adopt.

These observations also have implications for our educa-tional programs and how we train the next generation of ep-idemiologists. If the leading epidemiology journals focusinsufficiently on significant areas of population health, weas a discipline may fall short on our self-definition and ourpromise as a field. This stands both to change the composi-tion of those who are attracted to the discipline and poten-tially to influence the structural factors (such as promotionexpectations and criteria) that stand to reinforce our areasof focus and growth in the field going forward.


Am J Epidemiol. 2015;182(2):93–104



Our analysis has limitations. First, our results and interpre-tation depended on our selection of epidemiology journals. Adifferent list of journals could be considered. For instance, thePublic Health/Health Administration Section of the MedicalLibrary Association considered 10 journals as “essential for acollection that supports a program with subject specializationin this area” (30, p. 572). Moreover, many epidemiologic ar-ticles are published in nonepidemiologic journals. However,in our ancillary analysis of 6 high-impact general medicinejournals, we found patterns of epidemiologic papers thatwere consistent overall with the findings in epidemiologicjournals, which suggests that the trends observed here holdacross the discipline. Second, our results correspond to amacroscopic rather than microscopic mapping of the disci-pline in the sense that we may have missed subtle regularitiesin the objects of research. We were, in fact, interested by theidentification of main topics, suggesting that our approachsuited our purpose. We may have missed the exact dynamicsof appearance or disappearance of these main topics becauseour categorization of articles aimed for an approximatelyequal number of articles in each time period.In sum, we identified the major topics in 5 high-impact jour-

nals of epidemiology, and we analyzed the trends of thesemain topics. This allowed for an empiric perspective on thediscipline’s past, with an eye to its future. Our metaknowledgeinvestigation, which relied on freely accessible data sourcesand free software, is replicable. Monitoring the evolution ofthe science of epidemiology may help inform our efforts toconsider appropriate recalibration of the field’s scope.

ACKNOWLEDGMENTS

Author affiliations: Department of Epidemiology, MailmanSchool of Public Health, Columbia University, New York,New York (Ludovic Trinquart); and School of Public Health,Boston University, Boston, Massachusetts (Sandro Galea).Conflict of interest: none declared.

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