Health data and vital statistics together enable countries to understand their burden of disease and any changes in mortality rates and causes over time. Having this information is critical for health policy and program decision-making.
Explicitly examining data for differentials in sex, gender, and other defining characteristics such as ethnicity and subnational location is critical to ensure everyone is counted and their needs are addressed. Looking for differentials by disaggregating data can uncover inequities and lead to their prioritization and remedy.
United Nation member states have agreed to disaggregation of some health and development data. The United National General Assembly Resolution 68/261 states: “Sustainable Development Goal indicators should be disaggregated, where relevant, by income, sex, age, race, ethnicity, migratory status, disability, and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics.”
Datasets to examine
Birth, stillbirth, death, cause of death, and related health data may come from multiple sources within a country’s health information system. Health data sources can be classified as routine or non-routine. Routine health data is collected at regular intervals, such as monthly outpatient health data. Non-routine data is collected at different intervals and includes household surveys, censuses, and civil registration and vital statistics.
Looking at data from across the health information system is important to get the full picture of the burden of disease and to identify, prioritize, and address inequities. Data sources that will inform an understanding of health, mortality, and gender in a country may include:
|Routine health information system (in many countries this is the District Health Information System 2, or DHIS2)||Vital Statistics|
|Integrated disease surveillance and response system (IDSR), sometimes integrated in DHIS||Census|
|Demographic Health Surveys (DHS)|
|Multiple Indicator Cluster Surveys (MICS)|
Some countries capture mortality and cause of death data through other systems in addition to the health system. These data are not always collated and harmonized across systems, leading to underreporting of deaths. This is particularly the case regarding deaths due to accidents and violence, including gender-based violence. Some of these data come from other systems and sectors, such as police department or justice system databases. Gender-based violence fatality data in particular is severely underreported in most countries. There are also gaps in linking data on victims and perpetrators of violence in countries that report some of these deaths through the health system and others through different systems that then feed into vital statistics.
Data must be disaggregated and contextualized appropriately for each specific context to avoid perpetuating biases and structural inequality. It is important to collect and disaggregate death and causes of death data by sex, age, and other context-specific categories of importance such as ethnicity, disability, refugee status, sub-national location, income or wealth quintile.
Data are not neutral; it is important to recognize their limitations and biases. Structural biases inform what data are collected and how they are aggregated (e.g., gender and ethnicity categories) and can consequently mask disparities and reinforce health inequalities.
For example, for many years the US Census lumped together people who identified as Asian, Pacific Islanders, Native Hawaiians, and Native Americans. The aggregation of these data masked inequalities in health outcomes, particularly among Native populations.
Disaggregating data by sex and age is just the first step in identifying gender differentials in health indicators. Identifying sex and age differentials may expose inequalities and raise additional questions to explore in existing data or guide new data collection to better understand the situation. There may be specific gender indicators in some datasets, or gender proxy indicators, which can be used for more complex analyses of vital event data.
Understanding differentials in mortality data is critical for policymakers and public health implementers to develop targeted policies and interventions to improve health.