"One Health" or Three? Publication Silos Among the One Health Disciplines

Abstract

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation--especially along the veterinary/ecological research interface--remains.

Fig 1. Participant diversity and publication growth.

(A) Proportion of lead author affiliation disciplines across all 1,551 papers published in journals in the three major journal communities. “Math” here encompasses “math” and “stat” affiliations; “ecol” encompasses “eco,” “evo,” and “biol” affiliations; “vet” captures “vet,” “animal health,” and “animal science;” “Med” captures “med” and pharmacy affiliations. (B) Number of papers captured by our search through time. Blue = veterinary community; gold = ecology community; red = group 3. Numbers are the annual percent growth rate within each community. Data to generate this figure are contained in S1 Data.

Fig 2. Venn diagrams of cross-community authorship through time.

Each year’s Venn diagram is scaled to reflect the number of authors with two or more papers in our paper bank over the preceding 5 y. Number of authors with two papers in the same journal community are represented by disjointed regions of the circles, and number of authors with papers in two different communities are represented by the area of the intersections. Each circle is scaled to reflect the total number of authors with papers in that community during the 5 y prior to the label year. Areas are on a log-scale, and total number of authors with multiple papers each year is reported below each Venn diagram. Data to generate this figure are contained in S1 Data.

Fig 3. Model objectives from the three journal communities.

(A) Study system: agricultural (domestic animals), human, hypothetical, plant, or wildlife. (B) Applied, basic science, or management objectives by community. “Applied science” was used to describe scenarios in which basic science questions were addressed using systems of management interest. (C) Predictive or descriptive modeling intent. Error bars depict 95% binomial confidence bounds. Data to generate this figure are contained in S2 Data.

Fig 4. Cross-disciplinary citations through time.

(A) Citations from papers in ecology journals to papers in each journal community. (B) Citations from papers in veterinary journals to papers in each journal community. (C) Citations from papers in Group 3 journals to papers in each journal community. Shaded regions are 95% confidence intervals from a Poisson generalized additive model fit to each journal community's time series. Data to generate this figure are contained in S1 Data.

Fig 5. Citation benefits of author diversity.

Associations between author diversity and citation rate for papers in each journal community. Model estimates are derived from a Poisson mixed effects model with an offset term for years since publication, and coefficient estimates are reported in S8 Table. Predictions are calculated for papers published in 2010, with 25% of citations to other journal communities and 75% of citations to the paper’s own community. Data to generate this figure are contained in S1 Data.