Sebro, Ronnie A

Abstract Objective Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports. Materials and Methods This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P < 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth. Results 2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold. Discussion Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports. Conclusion This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.

AbstractThe inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

Objective: This study is a follow-up to a study in 2020 that reviewed changes in the racial and ethnic composition of public health students, graduates, and faculty among Association of Schools and Programs of Public Health (ASPPH)–member institutions. In the current study, we evaluated how the racial and ethnic composition among biostatistics and epidemiology students, graduates, and faculty changed from 2010 to 2020. Methods: We analyzed data on race and ethnicity of enrolled graduate students, graduates (master’s and doctoral), and faculty at ASPPH-member institutions by using institutionally reported data from the ASPPH Data Center. We tabulated frequencies, percentages, and percentage-point changes by race and ethnicity. We measured differences between groups by using a test for difference in 2 proportions. Results: The number of enrolled students, graduates, and faculty in all departments increased during the study period, while the number of tenure-track faculty in biostatistics decreased. The percentage of enrolled Hispanic/Latino biostatistics graduate students increased from 5.6% in 2010 to 10.2% in 2020 ( P = .007), and the percentage of epidemiology graduates increased from 8.8% to 13.8% ( P = .008). We found no differences among other underrepresented racial and ethnic groups. Most biostatistics and epidemiology professors at all ranks were non-Hispanic White, despite substantial decreases. The percentage of underrepresented racial and ethnic minority biostatistics and epidemiology professors was constant across all ranks. Conclusion: Although more Hispanic/Latino students are enrolled in and graduating from biostatistics and epidemiology departments at ASPPH-member institutions, we found no change among faculty. More work is needed to recruit and retain other (American Indian/Alaska Native, Black or African American, Native Hawaiian/Other Pacific Islander) underrepresented students and faculty.