Racial, Gender, and Size Bias in a Medical Graphical Abstract Gallery: A Content Analysis

Abstract

Introduction: Graphical abstracts may enhance dissemination of scientific and medical research but are also prone to reductionism and bias. We conducted a systematic content analysis of the Journal of Internal Medicine (JIM) Graphical Abstract Gallery to assess for evidence of bias.

Materials and methods: We analyzed 140 graphical abstracts published by JIM between February 2019 and May 2020. Using a combination of inductive and deductive approaches, we developed a set of codes and code definitions for thematic, mixed-methods analysis.

Results: We found that JIM graphical abstracts disproportionately emphasized male (59.5%) and light-skinned (91.3%) bodies, stigmatized large body size, and overstated genetic and behavioral causes of disease, even relative to the articles they purportedly represented. Whereas 50.7% of the graphical surface area was coded as representing genetic factors, just 0.4% represented the social environment.

Discussion: Our analysis suggests evidence of bias and reductionism promoting normative white male bodies, linking large bodies with disease and death, conflating race with genetics, and overrepresenting genes while underrepresenting the environment as a driver of health and illness. These findings suggest that uncritical use of graphical abstracts may distort rather than enhance our understanding of disease; harm patients who are minoritized by race, gender, or body size; and direct attention away from dismantling the structural barriers to health equity.

Conclusion: We recommend that journals develop standards for mitigating bias in the publication of graphical abstracts that (1) ensure diverse skin tone and gender representation, (2) mitigate weight bias, (3) avoid racial or ethnic essentialism, and (4) attend to sociostructural contributors to disease.

Keywords: gender; graphical abstracts; race; science communication; size bias; social determinants of health.

CASE 1.

Gruppen and colleagues describe results from a study and meta-analysis examining the association of two inflammatory biomarkers—GlycA and hsCRP—with overall and cause-specific mortality. They found that GlycA is significantly associated with all-cause mortality and that an identified association of GlycA and hsCRP with cancer mortality appears to be driven by men. The abstract for the study shows a large-bodied man, colored in bright purple, slouching in a sofa chair, smoking a cigarette. Although his facial features are absent, fat accumulations in his chest, abdomen, and suprapubic area are highlighted. To the man's right, on the arm of the sofa, is a hamburger, colored in yellow, and at his feet are a soda with a straw and a container of French fries, also colored in yellow. The posture of the male figure suggests fatigue, even laziness or inactivity; however, the authors did not consider physical activity in their analyses. The man's body is also large, implying obesity, yet the effect of BMI is only briefly mentioned, suggesting that BMI increased at higher GlycA levels (p. 599). In addition, the figure is holding a cigarette even though smoking status was either a control variable or not mentioned in all of the studies included in the article. Finally, the graphical abstract suggests that diet is an important contributor to the author's findings by emphasizing the hamburger, soda, and French fries, yet “diet” is only mentioned once in the article. These artistic decisions have the effect of suggesting behavioral contributions to increased mortality when, in fact, the authors only noted an association with inflammatory biomarkers. The depiction of a large-bodied person as indolent, surrounded by fast food, reinforces weight bias by suggesting this man is responsible for his own imminent mortality. BMI, body mass index; hsCRP, high-sensitivity C-reactive protein.

CASE 2.

Yaghootkar et al. review the genetics literature on “ethnic differences” in adiposity and risk for type 2 diabetes. Their discussion assesses the potential contributor of ethnic variation in genetic variants associated with increased fat storage to explain their presumption that “non-Europeans” are more susceptible to diabetes relative to Europeans. The graphical abstract accompanying Yaghootkar et al. illustrates two problems surrounding conceptions of ethnicity: First, the article and the graphical abstract use ethnicity as a proxy for unspecified risk factors; second, the authors assume that small quantities of human genetic variation are distributed in an ethnically discontinuous manner and correspond to differences in the risk for disease. The focus of the abstract, as the headline notes, is “ethnicity and diabetes.” Ethnicity is not defined, and none of the complexities of that concept is captured in the graphical abstract, leaving readers to fill in the gaps with whatever assumptions they make about ethnicity and its relationship to disease. If readers are prone to interpret ethnic differences in health as a result of genetic variation, they will find encouragement for that view in the graphical abstract. The abstract depicts a process model that begins with “positive energy balance” and ends with either a lower or higher risk for cardiometabolic disease. Risk is simplified as a binary outcome, and the main determinant is whether people have more or fewer “favorable adiposity genetic alleles.” Genetic variation is color-coded into two categories, with a green double helix resulting in “healthy” adipose tissue and a red one leading to “dysfunctional” adipose tissue. Because no other influences on adiposity or cardiometabolic risk are depicted, the implication is that genetic variation is the key to ethnic differences in diabetes. Moreover, the representation of genetic variation as two color-coded double helixes promotes categorical thinking and implies that some alleles are intrinsically maladaptive, while others promote good health. The featuring of the double helix further obscures the environmental interactions with genetics that together engender disease risk.

CASE 3.

In the article itself, Gao et al. describe the CNTR, which “aimed to study the genetic and environmental contributions to complex diseases, with particular emphasis on cardiovascular diseases” (p. 300). An important design aspect of the CNTR is its attention to nongenetic (behavioral and environmental) factors to identify what Gao et al. describe as “lifestyle-discordant and concordant twin pairs” (p. 303). Among the lifestyle variables available for analysis are smoking, alcohol consumption, fruit and vegetable consumption, and physical activity (p. 302). The registry also includes a range of clinical and anthropometric measures (e.g., height, weight, waist and hip circumferences, blood pressure) and standard sociodemographic measures such as marital status and educational attainment (p. 304). In short, Gao et al. describe a fairly broad range of nongenetic influences on cardiovascular disease, and the science they summarize in the article conveys the importance of environmental modifiers of disease risk. Yet the graphical abstract gives a different impression. It includes only two visual elements: an illustration of twin sisters against a background of swirling double helices of DNA. None of the nongenetic contributors to disease described in the article or available in the registry itself is represented in the graphical abstract. CNTR, Chinese National Twin Registry.

FIG. 1.

Multidimensional scaling plot of codes related to disease risk. A multidimensional scaling plot demonstrating the co-occurrence of codes in two-dimensional space. Four predominant clusters appear: The blue cluster features disease processes, including diabetes, cancer, and endocrine disorders along with major lifestyle contributors such as physical activity and smoking. The yellow cluster includes cardiovascular and neuropsychiatric diseases along with genes and epigenetics. The aqua cluster groups infections and autoimmune disorders and the green cluster groups gastrointestinal disease and diet.