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Religion, ethnicity, and political ideology all lend themselves to the perpetration of mass atrocities by creating a sense of identity that sets up an Us/Them dichotomy. Atrocities are modelled here as arising from the motive of acquiring territory but augmented by other-regarding preferences that capture the role of identity. My empirical results using data for the period 1800–2020 confirm that all these identity-driven motivators are associated with mass atrocities, with religion being more powerful than ethnicity. Monotheistic religions (with the strong exception of Judaism) are seen to be associated with more mass atrocity deaths than (polytheistic) Hinduism, lending partial credibility to Hume’s (1757/2010) view on the intolerance of monotheism. While democracies are associated with fewer mass atrocities than autocracies, Christian liberal democracies are not. My statistical analysis rejects the popular presumption that Islam is more violent than Christianity. In fact, in the post-World War II (WWII) era, among the major religions Christianity has been associated with the most mass atrocity deaths. The results also show that mass deaths were higher in atrocities that took place in settler colonies, especially in the post-WWII period of decolonisation. Using mass atrocities as the metric of violence, the correlations found in the empirical work of this paper offer many new and surprising findings.

The incompleteness of race and ethnicity information in real-world data (RWD) hampers its utility in promoting healthcare equity. This study introduces two methods—one heuristic and the other machine learning-based—to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthcare.