Linking the dimensions of policy-related research on obesity: a hybrid mapping with multicluster topics and interdisciplinarity maps

Related work on obesity

Obesity as a research topic has attracted great attention in the scientific literature both because its complex nature and growing societal relevance worldwide. This observation also applies to secondary research designs akin to our own approach inasmuch their main objective is some synthesis or review of the domain. Various systematic reviews or metaanalyses (characteristic of the medical and health sciences fields) have been conducted on numerous aspects of obesity, even in the policy arena (such as interventions or prevention programmes). However, as our main interest was the bibliometric approach involving large-scale mapping of the discourse (as opposed to the systematic review design), we were screening the related literature for such contributions. In general, several bibliometric studies addressing the composition of obesity research (with the broadest scope of the term) and its development have been reported in field-specific venues. Khan et al. (2016) analyzed the growth of obesity literature and the collaboration patterns at the country level using the standard databases (PubMed, Google Scholar, Scopus, WoS). The growth and distributions of the research output over geographic areas, journals, research areas and languages indexed in PubMed was the subject of Vioque et al. (2010). Similar studies dealt with specific geographic regions, such as the Arab countries (Sweileh et al. 2014). These aspects were complemented with a more detailed analysis on co-author networks, journal citation networks and simple thematic maps, based on co-occurrences of frequent terms in Yao et al. (2018), also a study on a specific clinical aspect (the role of intestinal microbiota in obesity). The topical organization of (NHLBI-) funded projects and the productivity and impact of individual topics was the key issue for Nicastro et al. (2016). However, probably the most extensive approach, being also the most relevant to our present work, has been provided in a series of recent papers best exemplified by Cassi et al. (2017). The main objective of this work was a large-scale bibliometric mapping of the intellectual structure of obesity research in general, and contrasting it with the societal demands, also formalized into a topic map (based on European parliament questions) so that the concordances between the two maps could be analyzed. In other words, the relation between research questions and policy-related questions were investigated, creating close proximity to our research goals. Our work extends this line of inquiry in a different direction, focusing specifically on policy-related obesity research as a potential interface between science and public policy (as in decision making). This complementary role can also be claimed at the methodological level: the referred study used a well-known topic modelling technique (Latent Dirichlet Allocation, LDA), to cope with the complex and densely interrelated topical structure of obesity research. Here, we propose a different framework to handle thematic complexities, to accumulate further evidence on the fine-grained structure of the policy-centred subdomain.

Alluvial diagrams as multicluster topic development

Leveraging on the virtue of alluvials to represent multiple characteristics and their interrelations “at a glance”, we applied two different bibliometric document clustering methods to elicit the thematic structure of the research domain. The main idea was to model topical organization from different perspectives, and linking them together (and with the time dimension). As a way of “triangulation”, we selected methods that rely on differing indicators of document content, and are simple enough in themselves to keep the formation of dimensions transparent for interpretation.

Clusterings used in the mapping procedure

We used classical bibliographic coupling (BC-clustering), as a link-based method, and a clustering over semantic similarities (Kw-clustering), as a text-based method. By virtue of this dual approach, our exercise could be viewed as utilizing a “hybrid method” for document clustering [where link-based and text-based methods are being combined for higher precision, cf. Boyack and Klavans (2010)], although with a significant alteration (see below). The two clustering procedures are detailed below.

Text-based method as a different perspective on the thematic structure of our domain, we applied a measure of textual similarity as well. To that end, we employed the keywords of documents attributed by authors (author keywords, the field labelled with “DE” in the WoS databases). Despite the well-known indexer effect as a major criticism of the use of author keywords, we were expecting an acceptable level of representation of the most relevant concepts in this domain, that is suitable for complementing the link-based results. The clustering procedure consisted of the following steps. (1) Author keywords have been retrieved from documents, and subjected to a standard normalization procedure (minimal version of natural language processing) via word stemming. (2) Documents have been represented as vectors of these stemmed keywords, based on which their pairwise similarity was calculated. The measure applied in this case was Salton’s cosine similarity, as a broadly used normalized indicator for comparing word vectors for their similarity in document clustering. (3) From the similarity matrix, a (textual) similarity (or proximity) network of documents was created, and the actual clustering exercise was identical to the one used for the link-based method: the fast-greedy algorithm has been run over the proximity network to gain coherent groups of papers (in terms of keyword usage).

Interpretation and labelling of clusters

A crucial point in our approach to link different thematic perspectives was the interpretation of the topical or semantic clusters obtained from the various classifications. As with the clusterings, we addressed this task from different angles: (1) we characterized the clusters with the most frequent title words occurring in the title of papers belonging to the cluster, and (2) obtained the most representative papers of clusters, using the so-called “core documents” methodology (see below).

As to the first method (1), for each group (BC-clusters and Kw-clusters) title words have been stemmed, and those pertaining to the (inverted) first quartile of the frequency distribution of words within the cluster were kept as eligible for labelling. Using title words provided the advantage to have a semantic descriptor for the groups independent of the descriptors underlying group formation (references, keywords), and commonly used for both clusterings.

For triangulation, (2) a second round of labelling was achieved by using the “core documents” methodology as proposed by Glänzel and Thijs (2012). According to their definition, core documents are those elements of the cluster that “have at least n > 0 links of at least a given strength r ∈ (0, 1) according to the predefined similarity measure”. That is, these are “similar enough” to most papers in the cluster, given the thresholds (n, r) being set properly. We determined these parameters clusterwise, so that a paper had to be similar to at least 40–60% of the cluster in order to belong to the core, with a similarity value above the third quartile of the distribution of similarities (upon which the clustering was achieved). The experimentation resulted in a reasonable number of core documents somewhat proportional to the cluster size (see “Results and discussion” section). To further sharpen the meaning of clusters, we coded the core documents for their characteristic terms (with the help of two field experts among the authors of this paper), and applied this term set in the labelling of clusters.

Based on the results of these two methods, labelling was done manually by outlining the most common theme unifying the cluster. The interpretation of the labels was validated by two field experts among the authors of the present work. In practice, the two aspects effectively complemented each other in identifying common topical dimensions.

Among the methodological advantages of this framework, we can consider the following. As is the case with hybrid methods, our approach sets out a multidimensional mapping of the domain, relying on multiple descriptors of publications. However, unlike the methods introduced so far, that combine the different types of link-based and textual similarities via a composite measure (Janssens et al. 2009), we connect different similarity structures by linking, instead of combining them together. Consequently, it does not yield a unique or “consensus” classification of publications into topics, but a multidimensional classification where the individual dimensions are linked and kept transparent. It might be more difficult to interpret, but, in exchange, requires no subjective decision on, or experimentation with trade-offs in setting the weight of different similarities in the composite measure. Also, loss of information due to dimension reduction is much less an issue. Still, with the interrelations of perspectives coded by the joint distribution of papers over multiple category systems a well-defined classification can be drawn.

Another feature we consider valuable in this framework is its rich potential to express thematic relations. First of all, in aligning two types of classifications one can be used to validate the other (high concordance between classifications or semantic similarity). Also, a more granular classification can further stratify the results of a more robust one, introducing hierarchic relations into the thematic structure. What’s more, the explicit categories yielded by the text-based method may be used as a semantic or conceptual characterization of the link-based categories, given their linkages. Perhaps more importantly, however, this kind of projection of the semantic dimension onto the link-based dimension allows for the discovery of several kinds of overlaps between topics. Although the individual clusterings does not handle topical overlaps, as popular topic detection techniques such as LDA, their interrelations do. A connection between two link-based cluster and a semantic cluster indicates their conceptual or thematic intersection, or, conversely, a conceptual system connecting differing research traditions (link-based clusters). On our account, the potential to reveal such complexities is an important strength of the alluvials-based approach.

Alluvial diagrams as interdisciplinarity maps

Basic statistics of the bibliographic record

Our query targeting the thematic intersection of obesity and policy retrieved n = 4340 documents. Some of the basic statistics for this record are reported in Table 1, indicating the average annual number of publications and journals for consecutive, equal-length periods, along with the standard deviation of these frequency distributions. Striking is the fact that both the mean number of publications and publication venues (journals) goes through an exponential-like growth, with a sudden increase in the last decade, as these periods witness an order of magnitude higher amounts than the previous ones. This dynamics is also mirrored in plotting the annual percentage share from the total amounts against publication years (Fig. 1), where the rather steep slope from the 2000s on indicate this high growth rate both in publication output and journal composition.

Table 1

Summary statistics of the publication output by time period

Period	Pubs		Journals
	Average	SD	Average	SD
(1991, 1997]	2.17	1.60	2.00	1.26
(1997, 2002]	8.00	3.29	6.50	2.26
(2002, 2008]	60.00	34.87	35.60	19.07
(2008, 2013]	263.17	76.14	106.67	22.87
(2013, 2019]	400.00	199.83	150.00	75.78

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Inspecting the “high end” of the journal distribution, i.e., the most frequent journals occurring in the (inverted) first quartile per year, 9 titles appear on the list (Table 2), with a main focus on public health and related areas (nutrition, environment, health promotion), preventive medicine and pediatrics. In a time perspective (Fig. 2), it is striking that in the decade between the early 2000s and 2010, Public Health Nutrition (PUHN) and the American Journal of Preventive Medicine (AJOPM) have been the leading venues, along with an increasing role of Pediatrics (PEDI), while from 2010 the BMJ Journal of Public Health (BMPH) became the most prominent publication venue, recently complemented with Plos One (PLOO) and the International Journal of Environmental Research and Public Health (IJOERAPH). So as to the volume of the output, despite the high and growing number of journals, research seems to be concentrated in a few leading venues characteristic of consecutive periods. Also, some indication of the growing and broadening scientific interest is also visible, so far as more specialized journals (in nutrition or pediatrics) are being gradually complemented by generalist ones (such as Plos One).

Table 2

The most frequent journal titles with the total number of published papers

SO	FREQ	ABBREV
BMC PUBLIC HEALTH	264	BMPH
PUBLIC HEALTH NUTRITION	73	PUHN
AMERICAN JOURNAL OF PREVENTIVE MEDICINE	38	AJOPM
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH	24	IJOERAPH
PLOS ONE	24	PLOO
PEDIATRICS	21	PEDI
HEALTH AFFAIRS	16	HEAA
AMERICAN JOURNAL OF HEALTH PROMOTION	6	AJOHP
FUTURE OF CHILDREN	6	FUOC

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The statistics on field composition (Table 3), narrowing the scope to the most frequent WoS Subject Categories in the usual first quartile, also conveys a high concentration in a dominant category (Public, Environmental and Occupational Health). A first impression on the theoretical frameworks can be gained through the dynamic view of this composition (Fig. 3). Aside from this dominant category (for better readability, we omitted this from the figure), some fields can be identified leading and almost “monotonically” increasing its role in the last decade: Health Care Sciences and Services (HCS&S), Business and Economics (Bs&E) and Nutrition and Dietetics (Nt&D) are the most prominent ones, while Pediatrics (Pdtr) or General and Internal Medicine (G&IM), while significant on the whole, has been fluctuating as to its rank in the annual profiles. A similar fluctuation is present for Agriculture (Agrc) and Food Science (FS&T) or Psychology (Psychl) with low annual shares, while Educational Research (E&ER) is trending in recent years.

Table 3

The frequency of occurrence of Web of Science Subject Categories (type: n ≈ 240) in the corpus

WoS SC	Freq	Abbrev
Public, Environmental & Occupational Health	7076	PE&OH
Health Care Sciences & Services	1417	HCS&S
Nutrition & Dietetics	1044	Nt&D
Pediatrics	959	Pdtr
Business & Economics	919	Bs&E
Education & Educational Research	710	E&ER
Biomedical Social Sciences	573	BmSS
General & Internal Medicine	524	G&IM
Environmental Sciences & Ecology	353	ES&E
Agriculture	316	Agrc
Social Sciences—Other Topics	254	SS-OT
Psychology	247	Psyc
Public Administration	241	PblA
Food Science & Technology	149	FS&T

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The multidimensional characterization of thematic development

The size distribution of clusters is given by the margins of Table 6 (a confusion matrix of the two classifications, see below), where cluster #0 contains the “unclassified” part of the corpus due to the lack of either references or author keywords in the database. It can be seen that both techniques resulted in a reasonably balanced structure in that most clusters consist of several hundreds of documents, while some small groups emerged from both procedures. Bibliographic coupling provided a much higher “coverage” over publications, i.e., a much fewer amount of unclassified cases, which in itself demonstrates the complementary roles of these techniques. Labelling the clusters demanded the joint interpretation of textual profiles (frequent title words) and core terms, given the significant overlap in title word sets, that, to some extent, prognosticated the high thematic interrelatedness among clusters (see below). Also, title words in several cases much less characteristic of the main theme, than core terms and documents (cf. BC cluster #5). However, the most frequent title word(s) in most cases seemed congruent with the common topic characterized by core document titles (for the full list of core documents and small cluster items, cf. Tables 11, 12, 13 and 14 in the “Appendix”).

Table 6

Confusion matrix of the two clusterings (Kw vs. BC-clusters)

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Our primary question, (RQ1), is addressed by evaluating the joint distribution of publications along both clusterings plus the time dimension, using alluvials. The alluvial diagrams created for this purpose are being shown in Fig. 4. For a better readability, three versions of alluvials has been made: (A) the first one is the simplest, “two-way” diagram, that only shows the relationships between the two clusterings. Given only two dimensions, the joint distribution of publications can also be represented by a so-called confusion matrix, a cross-tabulation of the two clustering variables, where cell values contain the number of papers in the intersection of the corresponding clusters—for the first alluvial, this matrix is reported in Table 6, where the color code (shades of blue) highlights the strength of the relationship. (B) The second one is already of the “three-way” form proposed in this paper, incorporating the time dimension, that is, it shows the linkages between publication years and the two clusterings. (C) The third type of alluvials only differs from the second one in the granularity of the time variable, as publication years are being aggregated into approx. 5-year periods. The reason for this is to have a more robust picture on how topics are distributed along the timeline. Robustness was also the reason behind setting a constraint on which stripes (publication sets with a given publication year and cluster memberships) were left visible on the diagrams: a minimum of 10 papers per year was specified as a threshold. Beyond connections, block sizes also convey a useful information, as these are proportional to the frequency of category values (cluster sizes or weight of publication years). As to the terminology, a topic will be referred to as a connected set of BC- and Kw-clusters, i.e. a “multicluster”, within which individual stripes are considered as subtopics. In order to improve the clarity of visualizations, we omitted the cluster #0 from both clusterings, that is, the cluster containing the unclassified cases in the respective grouping.

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Quantitatively speaking, both the two-and three-way diagrams exhibit a relatively high concordance between BC and Kw clusters: the largest proportion of BC-clusters or research traditions (i.e. the widest, or the majority of stripes) is being classified under a single corresponding Kw-cluster or semantic class. The concordance of the two classifications is also reflected by the coloring of the confusion matrix (Table 6), conveying the overlap between BC- and Kw-clusters in document coverage. The majority of documents in most major (sizable) clusters is being concentrated in a single cell, indicating a basically (though not technically) one-to-one relationship between the respective clusters. In order to make this pattern more explicit, we constructed a two-mode similarity network of the two clusterings (Fig. 5), with two sets of nodes representing BC- and Kw-clusters, respectively, and edges showing an overlap between two such nodes. The width of an edge is proportional to the extent of the overlap (from the BC-perspective), and red links indicate that at least 40% of the papers in the BC-cluster is covered by the corresponding Kw-cluster. The visualization also reinforces the match between the clusterings, since red lines tend to exhibit a one-to-one assignment (BC_3 and Kw_3; BC_6 and Kw_1 etc.), as BC-clusters tend to strongly connect to one Kw-cluster. Kw-clusters, or “subtopics”, on the other hand, seem to be more common, but, as to strong connections, still having an affinity to only one or two BC-groupings.

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In order to provide an overall quantitative evidence for the concordance, we have calculated the Rand index between the two groupings. The Rand index (abbreviated here as R) is a statistical measure of the similarity between two clusterings of the same dataset (the technical definition is given in the “Appendix”), and its value ranges between [0, 1]. The closer its value to the possible maximum, R = 1, the better the agreement between the two groupings. For our classifications, we obtained a value of R = 0.7 as the degree of similarity between the BC- and Kw-clustering, which, being close enough to unity, is generally considered to be a sign of good alignment.

Beyond the correspondence between the classifications, the next important observation (via the three-way diagrams) is that almost all topics are concentrated in the last 10-year period, and that almost all topics show an increase (in size) in the last 5 years, so that an upward trend emerges for the whole topical structure. This is very much in line with the rising curves obtained for publication numbers for this period, showing that this increase is multidirectional, covering a diverse and balanced thematic composition of this output.

Qualitatively, or content-wise, in order to elicit the thematic development of our domain, we follow the strategy of interpreting each multicluster topic relying on the interrelations of BC and Kw clusters (i.e. interpreting the two-way alluvial diagram using the cluster labels), and then linking them to the time dimension (i.e. via the three-way alluvial diagrams). The most salient BC cluster, #3 on risk factors of childhood obesity is mostly linked to the Kw cluster #3 on risk factors of overweight, children, and, to a smaller extent, to Kw #2 on public health policies and obesity prevention. The topic is constantly present since 2007, but increases its weight in the last five decades. The second biggest BC cluster, #6 focusing on physical activity and health promotion has a similarly strong affiliation with a specific Kw cluster, #1 on physical activity, built environment and active living, but this topic is somewhat more stratified, as BC#6 is, to a letter extent, also connected to Kw#2 (prevention) and Kw#3 (overweight). This topic is also abundant in the last decade, but reaches a higher volume in the recent period (with all subtopics). This high level of concordance can also be observed between BC#4 on economics of food policies (food policy, farm policy, taxation, public economic policy on consumption) and Kw#4 on food environment in school nutrition, where smaller proportions of the BC cluster are again linked to two further Kw blocks (Kw#2 on prevention and Kw#3 on overweight). Its subtopics mostly pertain to the last 5-year period, while the first appearance of the major subtopic (food environment) is in the previous interval. Screening the diagrams further, BC#7 with food marketing and communication policies, consumer behavior tends to be divided between several Kw clusters,—resulting in a semantically multifaceted topic—including Kw#2 (prevention) and Kw#4 (food environment) as the more prevalent subtopics, as well as Kw#3 (overweight) and Kw#5 (Childhood obesity prevention, active living and community-based instruments), in smaller proportions. The rather complex topic is clearly a product of the last period, within which it is equally distributed between the consecutive years. Cluster BC#8 on School nutritional policies and instruments, competitive and healthy nutrition closely resembles BC#7 in its semantic composition, with an even more equal distribution among the same Kw clusters (Kw#2, Kw#4, Kw#5, Kw#3), and with its subtopics also concentrated in the most recent period, as a relatively new topic. The last two prominent BC clusters (similar in terms of size) constitute a topic with one or two Kw clusters: BC#5, food environment, is practically aligned with Kw#4 (food environment in school nutrition)—aside from some subtopics being classified under Kw#3 (overweight) and Kw#2 (prevention), whereas BC#2 on interventions and particular, community-based programmes (especially for childhood obesity) has weak connections to Kw#2 and Kw#5, that is, the cluster on childhood obesity prevention, active living and community-based instruments, respectively. Again, both topics, BC#5 and BC#2 expands in the last 5-year interval (being concentrated mostly in most recent 3-year period).

Upon this overview of the resulted structure, several observations can be made. Firstly, the two clusterings (topics and subtopics) show a fairly coherent thematic picture, with subtopics fitting into, and elaborating on the meaning of general topics. Secondly, although the whole spectrum of factors and determinants of obesity, both at the individual (e.g. dietary patterns) and various community and economic levels (sociodemographic factors, schools, education, consumption etc.), can be seen as continuously trending, the dominant theme associated to most factors is that of food environment, and school-related issues. This two general theme seems to be the main focus of recent approaches to obesity form policy perspectives. These themes relating various BC-clusters also provides evidence that the topics are densely overlapping. This interrelatedness is further confirmed by the other (semantically defined) subtopics being common to many research directions (BC-clusters). Finally, some small BC- and Kw-clusters are underrepresented in the alluvials, showing scarce connections (under the thresholds set for stripes be visible), such as the quite coherent BC-theme on trade and globalization as a factor of obesity as a global disease (#9), or the very small but distinctive BC-cluster on internet behavior (#11). This fact underlies the role of the original clusterings to complement the “dominant” structure outlined by cluster interrelations.

Key concepts connecting research fields: interdisciplinarity and its development

In order to address (RQ2), another type of three-way alluvial diagrams has been applied. Similarly to the multicluster view, these diagrams connect the time dimension (in the aggregated form) with two further variables, key concepts and research fields (WoS Subject Categories) through their associations in the publication output under study. Since we are primarily interested in the patterns of interdisciplinarity, the role of key concepts in connecting research areas, we designed the alluvials to highlight these interconnections. As many concepts with high connecting potential are very much distributed among research fields, but would be suppressed in the alluvials by concepts with higher frequencies in the related categories, two types of diagrams have been created: the first one reports the main trends with concept occurrences above 10/category are made visible only (Fig. 6), and the other conveying latent trends where occurrences below this threshold are visible only (Fig. 7, “Appendix”). Concepts are sorted into different diagrams for better readability, only. Among the author keywords of the corpus about forty (n = 39) key concepts were identified, according to the procedure described in the “Data and methods” section, with a diversity threshold H > 1.5 and a frequency threshold F > 100 (based on the distribution of both values). These key concepts are listed in Table 8 (in their stemmed version), along with the indicator values applied for their selection. Concepts in the table are being ordered by diversity/entropy (that is, their distribution over research fields), the leading concepts bearing the highest potential in connecting fields. For contrasting the connecting role and the weight of key concepts, their frequency of occurrence is plotted against the entropy measure in the table.

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Table 8

The list and indicators of key concepts identified for the interdisciplinarity maps

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Note that some terms has been unified for the final analysis, such as ‘child”, “children”, or “bmi” and “body mass index”

In what follows, we will interpret the alluvial diagrams starting with the concept group contributing most to the multidisciplinarity of our domain (highest diversity values), which is practically the first column of Table 8. Filtering out the general themes (health, public health, nutrition, health policy etc.) we are left, in approx. the order of frequencies, with “obesity indicators” (body mass index, overweight), children as distinguished target group (childhood obesity, children, parent, and we can assign to this thematic axis adolescent as well), food policies (food policy, food, diet), and some prevalent factors and risks or regions of interest (socioeconomic status, diabetes, China, respectively). Concepts in the second column can be roughly assigned to these categories (see the detailed overview below), except physical activity, which is the most frequent key concept in the list, deserving a separate description.

Consulting the alluvials (Fig. 6), obesity indicators seem to serve as a common language among fields, connecting mainly Nutrition and Dietetics (through overweight but not the BMI, interestingly) and Pediatrics with Business and Economics and the Biomedical Social Sciences, but with stronger presence in the previous than in the most recent periods.

Childhood obesity and the related terms also link Business and Economics or Educational Sciences with Pediatrics, Nutrition and Health Care Sciences and Services with a constant prevalence in the last decade. The connecting role of parent is more extensive and latent, encompassing most areas with low frequencies per field, as shown in Fig. 7, but increasing its presence in the most recent period. On the other hand, school, while being a bridge between educational, clinical, nutritional and pediatric fields, mostly appears in between 2008 and 2013, which signals some shift of emphasis from institutional to family-related factors of childhood obesity.

Food policy as a term is rooted both in Business and economics and Agriculture, but seems more timely in connection with Nutrition and Dietetics, as the former stripes connect to only earlier periods. Fast food also appears as a less timely concept from mainly Business and Economics. Food environment, however, as a bridge between Education studies and Nutrition is a more recent framing of this perspective on obesity (linking to the latest period), while diet connects most fields (clinical, social and economic sciences) and concentrated in the last time interval. Sugar-sweetened beverages, as a specificity of this category generating much attention, is again a constant and highly multidisciplinary topic.

Physical activity is a prevalent concept in terms of size, and links the educational, clinical and health-related categories with pediatric issues. Its presence is continuous over time up to the very recent years. On the other hand, physical education is only attached to Educational Research (at least in the main trend diagram) with a similar distribution over the timeline.

Prevalent factors and risks of obesity exhibit a more subtle role in connecting areas, as the previous “massive” conceptual structures, in that many of them only expressed in the diagrams with latent connections (Fig. 7). Socioeconomic status as a key concept is equally present in all fields, and in the last decade, though with much lower frequency as per subject area. Diabetes behaves the same way, somewhat increasing its weight only within Nutrition in the last period. Distinctive in this respect is the concept of health disparities, at the intersection of Educational Research, Health Care Sciences, Nutrition and Pediatrics, as it connects these areas clearly in the most recent period. In contrast, nutrition transition and environment link most areas but in a less timely fashion. Finally, as a single concept signalling a joint attention on a geographic aspect of obesity policy, China has its related fields divided along the timeline, with an earlier contribution from Business and Economics, Agriculture and Biomedical Social Sciences, and with a most recent interest from clinical, health and nutritional sciences (G&IM, HCS&S, Nt&D).