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
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 directions 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 identified the main topics by clustering terms based on co-occurrence. Infectious disease and cardiovascular disease epidemiology 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 to methodology
and meta-analysis in genetics appeared during 2008–2013. Several areas of epidemiology, including injury or psychiatric epidemiology, did not make an appearance as major topics at any time. In an ancillary analysis of 6 highimpact 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
areas gaining or losing importance. Underrepresented research
paths might be due to a lack of attention to important areas that
need to be looked at in the future.
Metaknowledge investigations can complement the ongoing self-reflection in the field (10). Because they involve
analyzing large quantities of texts, metaknowledge investigations have the potential to allow the investigation of the
distribution and relative influence of topics over time (11).
Considering that what we, as epidemiologists, write should
reflect our vision of the discipline, such analysis may help
us shape the discipline. Therefore, we aimed here to provide
an empirical perspective on the field of epidemiology by
identifying the main topics in 5 major epidemiology journals
and assessing how they had evolved over the past 40 years.
The definition of epidemiology has not changed significantly
since it originated. A review of 70 epidemiology textbooks published between 1931 and 2014 shows that epidemiology has
consistently been defined as the science of understanding the
distribution and determinants of population health to be able
to intervene to control or prevent disease (Web Table 1 and
Web Figure 1, available at http://aje.oxfordjournals.org/). However, the scope of epidemiologic study and practice has expanded substantially over the past few decades. Between 1974
and 2013, there was a nearly 6-fold increase in the use of the
term epidemiology in papers indexed by MEDLINE.
Motivated in part by changes in funding opportunities and
in the scale of population-based studies, several recent comments have been concerned with potential future directions
for epidemiology as a discipline (1–7). However, much of
this soul-searching has been informed principally by expert
opinion, with little evidence to guide our thinking. An empiric perspective on the field’s evolution may be useful to
help guide our collective thinking about future research directions for the field (8, 9). A large-scale content analysis can
track how areas of epidemiologic research evolve, with various
METHODS
Selection of articles
We considered 5 high-impact epidemiology journals: the
American Journal of Epidemiology, the International Journal
93
Am J Epidemiol. 2015;182(2):93–104
94 Trinquart and Galea
of Epidemiology, the Annals of Epidemiology, Epidemiology,
and the European Journal of Epidemiology. Their impact
factors are among the highest for the category public, environmental, and occupational health of the Journal Citation
Reports, and these journals are widely considered the journals of record (12).
We retrieved from MEDLINE via PUBMED the records
of all indexed articles published in these 5 journals up to
2013, without any restriction on article type but including
only articles for which an abstract was available. The selected articles were categorized into 5 time periods, and
we aimed to have approximately equal numbers of articles
in each time period. Data were analyzed separately for each
period of time.
Linguistic processing
For each article, we extracted the title and abstract and then
combined them into a single string. We discarded the words
used to denote the structure of the abstract. Grammatical tagging allowed us to identify the part of speech (e.g., noun, pronoun, adjective, noun, or verb) and assign the lemma (its
canonical 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 different spellings of the same word (“ischaemic” and “ischemic”)
and for merging an abbreviation with the word or phrase itself
(PTSD and “posttraumatic stress disorder”). Each string was
then reduced to a set of noun phrases, that is, single nouns or
sequences of adjectives plus nouns or nouns that belong together (e.g., “cardiovascular disease” or “relative risk”). In
the 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 occurred
in fewer than 10 articles. The relevance of each term was estimated as the degree to which the occurrences of the term were
oriented towards 1 or more topics underlying the articles. For a
given term, it was measured as the Kullback-Leibler distance
between the distribution of (second-order) co-occurrences between that term and all other terms and the overall distribution
of co-occurrences over all terms. We selected the top 60% of
the terms with the highest relevance (13).
Mapping and clustering of terms
The selected terms were positioned on a 2-dimensional
co-occurrence plot and were grouped into clusters based on
the co-appearance of terms. A normalized co-occurrence frequency was derived for each pair of terms. The locations of
terms on the plot were determined by minimizing a weighted
sum of the squared distances between all pairs of terms. Minimization was achieved through stress majorization (14).
Consequently, terms with high co-occurrence tend to be close
to each other, whereas terms that are far away from each other
do not or rarely occur together in the same article (15). The
terms were also assigned to clusters using a weighted variant
of modularity-based clustering (16, 17). We characterized
each cluster by providing a heading based on the terms in
the cluster. We assessed the relative importance of clusters
according to their share of terms relative to the total number
of terms. Clusters of terms are interpreted as major epidemiology topics, and clusters located close to each other in the
map indicate related topics.
For each time period, the resulting maps show terms as labeled nodes in the co-occurrence network. Node size is proportional to the term frequency of occurrence, so that the
larger the node, the more articles include the term. The clustering of the terms is displayed on top of the map by coloring
nodes based on the cluster to which they belong. Analysis involved the use of the VOSviewer software, version 1.5.7
(Centre for Science and Technology Studies, Leiden University, The Netherlands) (18).
Identification of bursts
To identify topics that attracted attention in epidemiology
research but eventually faded away, we used Kleinberg’s
burst detection algorithm to identify words that experienced
sudden increases in use (19, 20). The algorithm assesses
states of the document stream, with different frequencies of
individual words, and identifies state transitions, that is,
years around which the frequency of a word’s usage changes
significantly. The analysis generates a list of burst words, together with the intervals of time during which each burst occurred and the intensity of the burst. We visualized the top
100 burst words graphically on a horizontal bar chart, with
publication year on the x-axis, burst words on the y-axis,
and a bar from the start to the end of the burst. The bar
width is proportional to the intensity of the burst. Bars
were color-coded according to the major epidemiology topics, as previously. Some words did not belong to any particular 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 Center, Indiana University, Bloomington, Indiana, http://sci2.cns.
iu.edu).
Ancillary analysis of high-impact general medicine
journals
Because many epidemiologic articles are published outside of epidemiology journals, we performed the following
ancillary analysis. We considered 6 high-impact general
medicine journals: The New England Journal of Medicine,
The Lancet, The Journal of the American Medical Association, The BMJ, Annals of Internal Medicine, and PLoS Medicine. To identify articles most likely of relevance to the field
of epidemiology, we analyzed how the articles published in
the 5 epidemiology journals were indexed with MeSH terms
in MEDLINE and we derived the following sensitivitymaximizing search filter: “epidemiology” (Subheading) OR
Epidemiologic Factors (MeSH) OR Epidemiologic Methods
(MeSH) OR epidemiologic studies (MeSH). Using this filter,
we retrieved from MEDLINE the records of articles with abstracts that were published in these 6 general medicine journals during the same time period that the other articles in the 5
epidemiology journals. The selected articles were categorized into the same 5 time periods. We applied the same linguistic processing to the titles and abstracts, and we mapped
and clustered terms into major topics, as previously.
Am J Epidemiol. 2015;182(2):93–104
Metaknowledge Analysis of Epidemiologic Topics 95
RESULTS
These 5 major topics were identified consistently over the
1990–1995 and 1996–2001 periods. In addition, a cluster
corresponding to female cancer epidemiology was identified
for 1990–1995 but disappeared thereafter, whereas a cluster
related to environmental appeared during 1996–2001 and
persisted in the subsequent periods.
For the period of 2002–2007, infectious and cardiovascular diseases epidemiology remained among the top major
topics. However, the cluster related to cancer epidemiology
disappeared, and the nutritional epidemiology cluster gained
a larger share of the term map. Moreover, a cluster related to
methodology appeared during 2002–2007, ahead of reproductive and perinatal epidemiology and environmental epidemiology, and persisted in the subsequent period.
Finally, 2008–2013 saw the appearance of another cluster
related to meta-analysis in genetics, and the period included a
total of 7 clusters. Cardiovascular diseases, nutrition, and infectious disease epidemiology remained the top major topics.
Reproductive and perinatal epidemiology and environmental
epidemiology completed the picture.
Characteristics of selected articles
We selected 20,895 articles. Overall, 42.7% were published
in the American Journal of Epidemiology, 21.7% in the International Journal of Epidemiology, 10.2% in the Annals of
Epidemiology, 10.9% in Epidemiology, and 14.5% in the European Journal of Epidemiology. Figure 1 shows the evolution
over time of the yearly number of articles across the 5 journals.
In all, 3,725 (17.8%) articles were published between 1974
and 1989; 3,948 (18.9%) were published between 1990 and
1995; 4,492 (21.5%) were published between 1996 and 2001;
4,180 (20.0%) were published between 2002 and 2007; and
4,550 (21.8%) were published between 2008 and 2013.
Mapping and clustering of terms
Figure 2 shows the mapping and clustering of terms over
time. Table 1 shows a summary of the clusters of terms and
the evolution of major epidemiology topics. The map for
1974–1989 contained 5 main clusters of co-occurring terms,
which corresponded to infectious diseases epidemiology,
cardiovascular diseases epidemiology, cancer epidemiology, reproductive and perinatal epidemiology, and nutrition
epidemiology.
Identification of bursts
The analysis of the 100 top burst words showed similar
patterns (Web Figure 2). From 1974 to 1999, all of the bursts
Journal
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Am J Epidemiol. 2015;182(2):93–104
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Reproductive and Perinatal
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 linguistic processing that were performed before the mapping and clustering of terms, as described in the Methods section. Node size is proportional to
the 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 or
rarely occur together in the same article, whereas terms with high co-occurrence are close to each other. The clustering of the terms is displayed on
top of the map by coloring nodes based on the cluster to which they belong. Clusters of terms are interpreted as major epidemiology topics, and
clusters located close to each other in the map indicate related topics.
Am J Epidemiol. 2015;182(2):93–104
Metaknowledge Analysis of Epidemiologic Topics 99
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
Table 1. Continued
1974–1989
1974–1989
Topica
Epidemiology
Journals 3,725
Articles, 951
Terms, %
General
Medicine
Journalsb 8,602
Articles, 823
Terms, %
Infectious disease
epidemiology
36
24
Infection
14
13
Antibody
8
6
Topica
Epidemiology
Journals 3,725
Articles, 951
Terms, %
General
Medicine
Journalsb 8,602
Articles, 823
Terms, %
Dose
7
Efficacy
6
Improvement
4
Health care quality
16
Physician
6
7
Care
4
Outbreak
6
Survey
4
Illness
6
6
Cost
3
5
Service
Virus
Prevalence
United States
3
1990–1995
4
Cardiovascular disease
epidemiology
24
Man
15
Smoking
7
Blood pressure
6
Cigarette smoking
5
Adjust
5
16
3
Risk factor
6
Relative risk
3
Diabetes
3
Cohort
2
Cancer epidemiology
15
Mortality
12
Cancer
11
Epidemiology
Journals 3,948
Articles, 1,116
Terms, %
19
Infectious disease
epidemiology
32
15
Infection
12
14
Antibody
5
5
HIV
5
7
Italy
4
Sensitivity
4
Detection
4
HIV infection
Cardiovascular disease
epidemiology
3
21
Mortality rate
4
Body mass index
Death rate
3
Cigarette smoking
6
Lung cancer
2
Blood pressure
5
Coronary heart disease
5
5
Therapy
14
General
Medicine
Journalsb 6,434
Articles, 903
Terms, %
18
8
Survival
5
Diabetes
Cell
4
Case control study
5
Chemotherapy
3
Baseline
5
Recipient
3
Adjust
5
5
Smoking
4
Hypertension
4
Reproductive and
perinatal epidemiology
12
Case control study
12
Relative risk
7
Confidence interval
6
Pregnancy
4
4
Infant
4
5
Mother
3
Birth
2
Delivery
2
Cancer epidemiology
18
Cancer
10
Approach
5
Mortality rate
4
Validity
3
Example
3
Reproductive and
perinatal epidemiology
14
8
4
Clinical trials
20
Pregnancy
6
Trial
10
Mother
5
3
Birth
5
4
Placebo
8
Table continues
Am J Epidemiol. 2015;182(2):93–104
Table continues
100 Trinquart and Galea
Table 1. Continued
Table 1. Continued
1990–1995
Topica
Epidemiology
Journals 3,948
Articles, 1,116
Terms, %
Infant
5
Smoker
4
1996–2001
General
Medicine
Journalsb 6,434
Articles, 903
Terms, %
5
Screening
5
Epidemiology
Journals 4,492
Articles, 1,346
Terms, %
Topica
Strategy
4
Survival
8
Cell
4
Nutritional epidemiology
8
Sensitivity
Consumption
8
21
Diet
5
Cardiovascular disease
epidemiology
Alcohol
4
Body mass index
10
Correlation
4
Food
3
Female cancer
epidemiology
4
Baseline
8
General
Medicine
Journalsb 6,891
Articles, 1,009
Terms, %
29
5
Physical activity
5
Hypertension
4
Alcohol consumption
4
4
Breast cancer
4
Diabetes
Parity
2
Case control study
5
Family history
2
Infant
4
Oral contraceptive
2
Birth
Menopause
1
Cancer epidemiology
Health care quality
27
5
4
15
Lung cancer
3
Care
9
Cigarette
3
Survey
8
Nonsmoker
3
Practice
6
Cancer registry
2
Questionnaire
6
Cancer risk
Physician
6
Reproductive and
perinatal epidemiology
Clinical trials
21
Therapy
14
Trial
14
Placebo
9
Efficacy
9
Dose
8
Cardiovascular disease
epidemiology
12
2
13
Pregnancy
7
Birth
7
Mother
5
Infant
5
Breast cancer
4
Nutritional epidemiology
10
Consumption
7
Sensitivity
5
Diet
4
Myocardial infarction
4
Validity
3
Stroke
3
Error
3
Specificity
3
Agreement
2
2
Environmental
epidemiology
6
Acute myocardial infarction
1996–2001
Epidemiology
Journals 4,492
Articles, 1,346
Terms, %
Infectious disease
epidemiology
36
Infection
11
Approach
5
HIV
4
Antibody
4
General
Medicine
Journalsb 6,891
Articles, 1,009
Terms, %
25
Asthma
2
Air pollution
2
Season
2
Respiratory symptom
1
Respiratory disease
1
Health care quality
13
6
9
Quality
7
Survey
7
Practice
Table continues
25
Care
7
Table continues
Am J Epidemiol. 2015;182(2):93–104
Metaknowledge Analysis of Epidemiologic Topics 101
Table 1. Continued
Table 1. Continued
1996–2001
Topica
Epidemiology
Journals 4,492
Articles, 1,346
Terms, %
2002–2007
General
Medicine
Journalsb 6,891
Articles, 1,009
Terms, %
Physician
7
Clinical trials
22
Trial
21
Placebo
11
Efficacy
10
Dose
7
Double blind
6
2002–2007
Epidemiology
Journals 4,180
Articles, 1,236
Terms, %
Nutritional epidemiology
24
Consumption
9
Diet
3
Inverse association
3
Incident case
2
Lower risk
2
Cardiovascular disease
epidemiology
General
Medicine
Journalsb 6,283
Articles, 978
Terms, %
22
Topica
Reproductive and
perinatal epidemiology
Pregnancy
Epidemiology
Journals 4,180
Articles, 1,236
Terms, %
General
Medicine
Journalsb 6,283
Articles, 978
Terms, %
10
7
Mother
6
Infant
4
Birth weight
4
Offspring
3
Environmental
epidemiology
7
Asthma
2
Air pollution
2
Nonsmoker
2
Susceptibility
2
Season
2
Health care quality
20
28
Practice
7
Health
7
Survey
6
Research
6
Problem
5
10
4
Clinical trials
Cardiovascular disease
6
5
Placebo
Weight
5
Controlled trial
12
Coronary heart
disease
5
Hazard ratio
10
Efficacy
10
Height
5
Body mass index
Dose
23
12
8
Stroke
5
Meta-analysis
13
Hypertension
5
Quality
10
Case control study
4
Review
6
Medline
6
Systematic review
6
Meta analysis
6
Infectious disease
epidemiology
20
Infection
8
Mortality rate
3
Transmission
3
HIV
3
Setting
2
4
Gene
4
Cell
4
Progression
3
Vaccine
3
Methodology
Cost-benefit analysis
2
Cost
5
Dollar
2
Cost effectiveness
2
Life year
2
Life expectancy
1
2008–2013
Epidemiology
Journals 4,550
Articles, 1,354
Terms, %
16
Approach
7
Epidemiology
5
Bias
5
Paper
5
Problem
4
Table continues
Am J Epidemiol. 2015;182(2):93–104
Cardiovascular disease
epidemiology
23
Body mass index
12
General
Medicine
Journalsb 6,159
Articles, 1,040
Terms, %
18
5
Table continues
102 Trinquart and Galea
Table 1. Continued
Table 1. Continued
2008–2013
Topica
Epidemiology
Journals 4,550
Articles, 1,354
Terms, %
2008–2013
General
Medicine
Journalsb 6,159
Articles, 1,040
Terms, %
Epidemiology
Journals 4,550
Articles, 1,354
Terms, %
Topica
General
Medicine
Journalsb 6,159
Articles, 1,040
Terms, %
Height
5
Season
2
Childhood
3
Temperature
1
Cause mortality
3
Hospital admission
1
Blood pressure
3
Meta-analysis
3
13
9
Cardiovascular disease
5
Meta analysis
5
Prospective cohort study
4
Gene
4
Smoking
4
Systematic review
3
3
Gene
8
Genotype
3
Nutritional
epidemiology
20
Polymorphism
2
8
Hazard ratio
10
Randomized controlled
trial
7
Consumption
5
Medline
Diet
3
Database
Health study
3
Global health
38
3
Country
11
Smoker
Infectious disease
epidemiology
19
Research
8
Epidemiology
6
Infection
6
Issue
4
Article
4
Methodology
15
Method
13
Approach
10
7
Prevalence
9
Trend
6
Survey
6
Cost
6
Clinical trials
20
Hazard ratio
15
Therapy
15
Week
13
Placebo
12
Clinical trial
11
Bias
7
5
Cardiovascular disease
epidemiology
13
Problem
5
Hazard ratio
15
Design
Reproductive and
perinatal epidemiology
11
Pregnancy
8
Mother
5
Childhood
4
Birth weight
3
Infant
3
Environmental
epidemiology
7
Air pollution
3
Particulate matter
2
Table continues
were related to infectious diseases except the words “systolic”
and “diastolic,” which were related to the epidemiology of cardiovascular diseases. The word “seropositive” showed the
most intense burst. After 2000, there was no clear time pattern
of burst. Some terms belonged to a single topic. For instance,
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 within
each cluster (with the percentage of articles including each term).
The major topics are ordered by their importance in the epidemiologic
journals and then in the general medicine journals.
b
For the 6 high-impact medical journals, we retrieved articles most
likely of relevance to the field of epidemiology by using a custom
search filter.
the words “ambient” and “particulate” were exclusively related
to environmental epidemiology. Eight terms were related to
genetics and meta-analysis. Two of the terms with the most intense bursts (“gestational” and “preterm”) were related to reproductive and perinatal epidemiology. A majority of burst
Am J Epidemiol. 2015;182(2):93–104
Metaknowledge Analysis of Epidemiologic Topics 103
words were related to methodology (e.g., “pathway,” “Bayesian,”
“causal,” “mediation,” and “simulation”). Finally, approximately one-third of burst words did not belong to any particular 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 relevance to the field of epidemiology that were published in
the 6 general medicine journals. Web Figure 3 shows the
mapping and clustering of terms over time. Table 1 shows a
summary of the clusters of terms and allows comparison with
articles from epidemiologic journals.
Some topics were similar to those identified in epidemiology journals and showed similar evolution. Cardiovascular
diseases and infectious diseases were among the main topics
over the 5 time periods, except the last time period for infectious diseases. We identified a cancer cluster in the 1974–
1989 period and a reproductive and perinatal health cluster
over the 1974–1989 and 1990–1995 periods. A cluster related to meta-analysis appeared during 2002–2007 and persisted in the subsequent period.
Other topics differed from those identified previously. One
cluster corresponding to clinical trial terms (in multiple clinical specialties) was identified over the 5 periods. Another
cluster related to health care quality was identified over the
periods from 1974 to 2007. Lastly, a cost-benefit analysis
cluster was identified in 2002–2007, and a global health cluster was identified in 2008–2013.
DISCUSSION
We analyzed 20,895 articles published in 5 epidemiology
journals over 4 decades using a production-oriented approach
to investigate the epistemic core of epidemiology. We found a
clear pattern of leading areas of epidemiologic inquiry during
this period and patterns in the evolution of these areas. We
found, first, that the epidemiology of infectious and cardiovascular diseases have consistently been the main topics of interest
in these 5 journals. Second, cancer epidemiology has been a
major topic, with a peak in knowledge production in 1990–
1995, where 2 clusters related to cancer and female cancer
were identified but stopped being a leading focus of papers in
the 5 epidemiologic journals after 2001. Third, nutritional epidemiology gained importance over time. Fourth, 3 topics were
among the leading areas of inquiry for time-delimited periods,
namely environmental epidemiology since 1996, whereas methodology and meta-analysis in genetics appeared in 2008–2013.
Because we focused our inquiry on these 5 leading epidemiology journals, we can interpret our findings as representing
knowledge produced and regulated through peer review principally by epidemiologists and shaped by editorial processes in
line with the leading epidemiologic organizations. The American Journal of Epidemiology is published in association with
the Society for Epidemiologic Research, the International
Journal of Epidemiology is published on behalf of the International Epidemiological Association, Epidemiology is the offiAm J Epidemiol. 2015;182(2):93–104
cial journal of The International Society for Environmental
Epidemiology, and the Annals of Epidemiology is the official
publication of the American College of Epidemiology. By design, this analysis, excludes papers published by epidemiologists, or papers published by nonepidemiologists that would
nonetheless be considered within the field’s remit, that were
published in nonepidemiologic journals. There is little question that such papers thrive, particularly in clinical journals.
So, for example, the decrease in focus on cancer in these 5
journals over the past decade represents most likely a shift in
where these papers are being published—away from epidemiology journals to cancer journals. We would argue, however,
that there is consequence to publishing the relevant papers
in the leading journals in the discipline. Epidemiologists
are, in many respects, the keepers of the methodological flame
in population health sciences. If cancer epidemiology is evolving in nonepidemiology journals, it represents a tremendous
lost opportunity for the field to make the contribution it can
and should make to one of the leading global causes of death.
Therefore, although our observations are in some ways heartening, reinforcing that we are focusing on cardiovascular disease commensurately with the contribution of cardiovascular
disease to burden of mortality, they also suggest that the discipline is playing a far smaller role in other areas that are also
important. For example, although several new areas have gained prominence in the field over the past decades, including social epidemiology, these are clearly not represented among the
key areas in these 5 leading epidemiology journals over the
time period of interest (21–23). Moreover, areas such as injury,
psychiatric, or neurological epidemiology are clearly not among
the main topics identified; this is dissonant with the importance
that these areas have for global burden of disease (24–26).
Within a consequentialist epidemiology framework, it would
certainly stand the field in good stead if we engaged actively
around inquiry concerning the major causes of morbidity and
mortality worldwide, with an eye to how we may prevent disease and improve health (27–29).
The increasing role that methodological papers play in
publication in epidemiology journals over the past decade
presents both opportunities and challenges. In some respects,
this evolution represents an evolution in the field, wherein
methods for epidemiology are being developed principally
by epidemiologists. This reflects a maturity in the field, moving well beyond its origins where methods in the discipline
emerged from other areas (8). However, it also suggests that
the field takes upon itself greater responsibility, both to keep
developing methods that are adequate to the evolving population health challenges we face and to ensure flexibility to
the incorporation of methods that do arise in other areas that
may be fruitful for epidemiology to adopt.
These observations also have implications for our educational programs and how we train the next generation of epidemiologists. If the leading epidemiology journals focus
insufficiently on significant areas of population health, we
as a discipline may fall short on our self-definition and our
promise as a field. This stands both to change the composition of those who are attracted to the discipline and potentially to influence the structural factors (such as promotion
expectations and criteria) that stand to reinforce our areas
of focus and growth in the field going forward.
104 Trinquart and Galea
Our analysis has limitations. First, our results and interpretation depended on our selection of epidemiology journals. A
different list of journals could be considered. For instance, the
Public Health/Health Administration Section of the Medical
Library Association considered 10 journals as “essential for a
collection that supports a program with subject specialization
in this area” (30, p. 572). Moreover, many epidemiologic articles are published in nonepidemiologic journals. However,
in our ancillary analysis of 6 high-impact general medicine
journals, we found patterns of epidemiologic papers that
were consistent overall with the findings in epidemiologic
journals, which suggests that the trends observed here hold
across the discipline. Second, our results correspond to a
macroscopic rather than microscopic mapping of the discipline in the sense that we may have missed subtle regularities
in the objects of research. We were, in fact, interested by the
identification of main topics, suggesting that our approach
suited our purpose. We may have missed the exact dynamics
of appearance or disappearance of these main topics because
our categorization of articles aimed for an approximately
equal number of articles in each time period.
In sum, we identified the major topics in 5 high-impact journals of epidemiology, and we analyzed the trends of these
main topics. This allowed for an empiric perspective on the
discipline’s past, with an eye to its future. Our metaknowledge
investigation, which relied on freely accessible data sources
and free software, is replicable. Monitoring the evolution of
the science of epidemiology may help inform our efforts to
consider appropriate recalibration of the field’s scope.
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology, Mailman
School 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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