This paper presents the first longitudinal study of sex disparities in COVID-19 cases and mortalities across U.S. states, derived from the unique 13-month dataset of the U.S. Gender/Sex COVID-19 Data Tracker. To analyze sex disparities, weekly case and mortality rates by sex and mortality rate ratios and rate differences were computed for each U.S. state, and a multilevel crossed-effects conditional logistic binomial regression model was fitted to estimate the variation of the sex disparity in mortality over time and across states. Results demonstrate considerable variation in the sex disparity in COVID-19 cases and mortalities over time and between states. These data suggest that the sex disparity, when present, is modest, and likely varies in relation to context-sensitive variables, which may include health behaviors, preexisting health status, occupation, race/ethnicity, and other markers of social experience.

Last spring, the US National Institutes of Health (NIH) announced a new policy calling for the use of both male and female materials-animals, tissues, cells, and cell lines-in preclinical research (1). Canada and the European Union have recently instituted similar policies. Advocates argue that requiring analysis of sex in preclinical research will advance scientific understanding of sex differences in human health outcomes, such as higher rates of adverse drug events (ADE) in women compared with men (2). We disagree.To be useful in addressing health disparities, sex-linked variables in preclinical materials must effectively model differences between human men and women. In the absence of evidence that this is so, the addition of sex as a variable in all preclinical studies is likely to introduce conceptual and empirical errors into research. Biomedical research institutions and funders can better remedy sex differences in health outcomes by focusing on the scientific study of the interaction of sex and gender variables in health outcomes in human populations.Sex differences in rates of ADE may be a result of biological factors, gender-related social factors, or a combination of sex-and gender-related variables. "Sex" refers to chromosomal complement, reproductive organs, or specific hormones related to sexual reproduction. "Gender" refers to sociocultural norms, expectations, and practices ascribed to males and females (3). Gendered factors, such as women's propensity to take multiple pharmaceuticals simultaneously (polypharmacy) compared with men, and their greater likelihood to see medical doctors than men, play a well-documented role in sex differences in health outcomes (4-6).Take the case of zolpidem (Ambien). In 2013, the Food and Drug Administration issued an unprecedented advisory reducing the recommended zolpidem dosage for women, following reports of higher numbers of ADE in women compared to men (7). Since then, researchers have sought the biological basis for this sex difference in reports of zolpidem-related ADE. Surprisingly, experimental studies of sex differences in the pharmacokinetics and pharmacodynamics of zolpidem in human men and women found that body weight, not sex, is the culprit. Women clear zolpidem from their system more slowly than men, but body weight eliminates the statistical significance of sex as a variable in clearance of zolpidem (8). Because body weight, not sex, is the independent biological variable, sex-based preclinical research protocols would likely not have predicted sex differences in rates of ADE with zolpidem.The zolpidem case provides an example of the need for studies aimed at uncovering the embodied interaction of human sex-and gender-related variables in sex differences in ADE. Weight is distributed differentially across male and female bodies. In presentday American populations, weight may interact with gender-related variables. For example, higher rates of zolpidem use and polypharmacy in women compared to men, as well as biopsychosocial factors, such as wome...

Background Inequities in COVID-19 outcomes in the USA have been clearly documented for sex and race: men are dying at higher rates than women, and Black individuals are dying at higher rates than white individuals. Unexplored, however, is how sex and race interact in COVID-19 outcomes. Objective Use available data to characterize COVID-19 mortality rates within and between race and sex strata in two US states, with the aim of understanding how apparent sex disparities in COVID-19 deaths vary across race. Design and Participants This observational study uses COVID-19 mortality data through September 21, 2020, from Georgia (GA) and Michigan (MI). Main Measures We calculate age-specific rates for each sex-race-age stratum, and age-standardized rates for each race-sex stratum. We investigate the sex disparity within race groups and the race disparity within sex groups using age-standardized rate ratios, and rate differences. Key Results Within race groups, men have a higher COVID-19 mortality rate than women. Black men have the highest rate of all race-sex groups (in MI: 254.6, deaths per 100,000, 95% CI: 241.1–268.2, in GA:128.5, 95% CI: 121.0-135.9). In MI, the COVID-19 mortality rate for Black women (147.1, 95% CI: 138.7–155.4) is higher than the rate for white men (39.1, 95% CI: 37.3–40.9), white women (29.7, 95% CI: 28.3–31.0), and Asian/Pacific Islander men and women. COVID-19 mortality rates in GA followed the same pattern. In MI, the male:female mortality rate ratio among Black individuals is 1.7 (1.5–2.0) while the rate ratio among White individuals is only 1.3 (1.2–1.5). Conclusion While overall, men have higher COVID-19 mortality rates than women, our findings show that this sex disparity does not hold across racial groups. This demonstrates the limitations of unidimensional reporting and analyses and highlights the ways that race and gender intersect to shape COVID-19 outcomes.

Background We are witnessing renewed debates regarding definitions and boundaries of human gender/sex, where lines of genetics, gonadal hormones, and secondary sex characteristics are drawn to defend strict binary categorizations, with attendant implications for the acceptability and limits of gender identity and diversity. Aims Many argue for the need to recognize the entanglement of gender/sex in humans and the myriad ways that gender experience becomes biology; translating this theory into practice in human biology research is essential. Biological anthropology is well poised to contribute to these societal conversations and debates. To do this effectively, a reconsideration of our own conceptions of gender/sex, gender identity, and sexuality is necessary. Methods In this article, we discuss biological variation associated with gender/sex and propose ways forward to ensure we are engaging with gender/sex diversity. We base our analysis in the concept of “biological normalcy,” which allows consideration of the relationships between statistical distributions and normative views. We address the problematic reliance on binary categories, the utilization of group means to represent typical biologies, and document ways in which binary norms reinforce stigma and inequality regarding gender/sex, gender identity, and sexuality. Discussion and Conclusions We conclude with guidelines and methodological suggestions for how to engage gender/sex and gender identity in research. Our goal is to contribute a framework that all human biologists can use, not just those who work with gender or sexually diverse populations. We hope that in bringing this perspective to bear in human biology, that novel ideas and applications will emerge from within our own discipline.

In the corrected version of their 2018 article, Stoet and Geary (Corrigendum issued 2019) responded to our identification of a mismatch between their numbers for women in science, technology, engineering, and math (STEM) with tertiary degrees and the United Nations Educational, Scientific and Cultural Organization (UNESCO, 2015) data they sourced. They clarified that their numbers do not represent the percentage of women among STEM graduates, as they had originally stated. Rather, their numbers represent a ratio, which they claim measures the "propensity" for women compared with men to earn a tertiary degree in STEM in a given country (p. 584). The use of this measure in combination with the Global Gender Gap Index (GGGI), a contested (Else-Quest & Hamilton, 2018;Hawken & Munck, 2013) composite measure of nationlevel gender equality increasingly employed in similar studies advancing the hypothesis of a gender-equality paradox (e.g., Falk & Hermle, 2018), raises methodological and empirical questions about their claims that there is a gender-equality paradox in STEM and that a larger gender gap in STEM achievement in high genderequality countries is evidence of baseline sex differences in career and educational preferences.