Sex is a crucial determinant of health, and it has been proven that disease outcomes, occurrences, and health care needs differ for women and men. Sex disaggregated data are typically collected and analyzed in two biological categories: males and females, and then used to develop programs and policies to improve health outcomes.
However, sex disaggregated health data does not consider gender identity, or gender roles, norms and relations, all of which also affect health outcomes. Sex disaggregated data tells you the numbers of men and women affected by a particular disease, condition, or cause of death. Gendered health data asks “the who questions:” Who does the household work, who has higher prevalence of a particular disease, and who is accessing the healthcare system to understand the root causes of health inequities in a population.
Gendered data is often confused with data disaggregated by sex. As it turns out, biological sex is a poor proxy for gender. It disregards the socially built power relations and gender norms that exist between and among males, females, and people with other gender identities, ignoring the cultural and systematic disparities that exist. Gendered power relations have significant influence on data collection efforts, health-seeking behaviors, vulnerability to risk factors for non-communicable and other diseases, and the impact of health policies. Sex and gender have independent and interactive consequences on individuals’ health, illness, and healthcare experiences. Furthermore, the binary of biological sex excludes gender identity and the group of people who identify as transgender, non-binary and intersex.
Here’s an example: We learned from the COVID-19 pandemic that while infection rates were similar in males and females, males were over two times more likely to have severe disease or die from the infection than females were. This has prompted further gendered analysis into the differences in COVID outcomes based on other demographic data such as age, socioeconomic status, and ethnicity to see how sex intersects.
In addition, data about the effect of COVID on non-binary people was absent in most countries. Gendered data analysis and discussion could confirm that non-binary people comprise one of the marginalized populations that had more severe outcomes during the pandemic.
Data collected within CRVS systems in most countries only considers the biological sex of individuals (i.e., male and female), leaving us with a deficit of information within death registries regarding cause of death for marginalized populations, including women, girls, and people who are transgender, non-binary and gender non-conforming. In addition, without considering the inequities in gender roles that affect women and people who are non-binary in many countries, death registries may be incomplete because more males have access to end-of-life care in hospitals, and fewer females have access to participate in the data collection process for deaths in their families.
Many programs and policies fail to disaggregate data by sex, and even if they do, they do not use the disaggregated data for gender-based analysis. A gendered data analysis can reveal the intersections of the sociocultural determinants of health outcomes and inform decision making about revisions to and development of new equitable health programs and policies.