Male/Female Is Not Enough: Adding Measures of Masculinity and Femininity to General Population Surveys

Survey research and sociological theory each provide insights into how and why people and groups act, think, and feel. Sociological theories identify what concepts are important for understanding and representing the social world. That is, sociological theories inform what to measure in surveys, and, to a certain extent, how to measure it. Survey research permits sociologists to carefully specify what is to be measured vis a vis sociological theory, setting surveys apart as a social research tool. It is this level of specification of concepts and measures that allow surveys to provide continued value at a time when “big data” proliferate. High quality survey measurement and estimation is necessary for sociologists to evaluate sociological theory among generalizable samples with well-developed questions, leading to further refinement and improvement of the theory and improved understanding of the social world. High quality surveys also provide insights into where sociological theories fail and where they must be adjusted for different subgroups, as well as basic insights into the prevalence of outcomes of interest. Together, sociological theory and survey methods produce insights about society that can inform decision-making and social policy. This mutually reinforcing relationship between sociological theory and survey methods requires sociological theory to evolve from insights obtained using survey methods and survey measurement to evolve with advances in in sociological theory. The measurement of sex and gender in surveys is one area where the development of survey measures has not kept pace with sociological theory and empirical, largely qualitative, findings. Contemporary gender theory sees sex and gender as separate concepts, both of which are important for understanding behaviors and outcomes. Yet, virtually all contemporary surveys measure sex as a binary “male” versus “female” categorization and fail to measure gender, ignoring important heterogeneity in gender identification that may exist within sex categories and any overlap that may occur across categories. Both gender scholars and survey researchers are potentially affected by this shortcoming of modern survey measurement. Gender scholars lose an important tool for assessing gender theories, especially on generalizable samples, risking conclusions that are specific to a small group of individuals rather than the population at large. Survey researchers risk producing theoretically obsolete data, limiting the utility of the data or potentially generating misleading conclusions. Survey data that fail to capture and reflect modern and complex understandings of our social realities also face increased risk of being replaced by “big data” such as administrative and social media data. Survey data that do reflect modern and complex understandings can bring value not available in administrative or other data and are therefore unlikely to be replaced. This paper is part of a growing chorus advocating for updates to how modern surveys measure sex and gender. We argue that the reliance on a single binary measure of sex (male or female) is out of step with current sociological understandings of sex and gender. In response, we propose and test a new theoretically-informed gradational measure of gender identification in a nationally representative mail survey. We evaluate whether respondents answer the gender measure and examine the reliability and predictive validity of the measure. In particular, we examine whether measuring gender gradationally adds explanatory value beyond sex on important social outcomes such as sexuality, childcare, grocery shopping, housework, working for pay, and military service. We also examine whether sex moderates the effect of gender identification in the ways that sociological theory would suggest on these outcomes

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer