* Any views expressed in this opinion piece are those of the author and not of Thomson Reuters Foundation.
As the international community shifts its focus from the Millennium Development Goals (MDGs) to the post-2015 development framework, it must start to address the woefully inadequate techniques for acquiring statistical information on health outcomes. Current data collection practices conceal the true health of millions of people, as the unique conditions faced by marginalised communities get lost in national averages.
Most countries currently track progress towards MDG targets through aggregated data, meaning that the data uses averages from the whole population. This means that the variations in health levels experienced by different ethnic groups are masked, which often inhibits targeted development programmes and provides little accountability to governments who may be failing the groups that are most in need. Additionally, the use of aggregate data may even encourage development efforts to focus on those who are the easiest and cheapest to reach, while leaving the poor and marginalised unaccounted for. This is having a hugely detrimental impact on the health of women and children from indigenous and other marginalised ethnic and cultural groups within developing countries.
Child health, the focus of MDG 4, provides just one example of a situation in which the needs of marginalised and indigenous communities are hidden. Ethiopia, which has one of the highest rates of child deaths, has seen under-five mortality drop to 88 per 1,000 from 118 per 1,000 ten years ago. Whilst such figures provide for positive reading, they also obscure the realities faced by hundreds of thousands of families. Whilst 88 child deaths per 1,000 is the average figure for the country as a whole, if we look specifically at the rate of child deaths in the eastern Somali region, where a high proportion of the country’s 12-15 million nomadic people live, we find that the figure is 122 deaths per 1,000 children. The rate of child death in this region is still higher than the national average was 10 years previously. ,. The use of aggregate data, therefore, presents a picture of overall progress when the reality is that some communities are still experiencing extremely high rates of death.
The disaggregation of data by ethnicity would help to reveal the truth about poverty levels and the health and well-being of millions of the poor and most marginalised groups worldwide, establishing a solid base for groups to advocate for health policies and programmes that address the particular and often severe needs of different groups.
The disparities in health provisions based on ethnicity are often a symptom of wider tensions within a country. The San people of Namibia, for example, experience significantly worse health outcomes when compared to the rest of the population. Whilst the government’s spending per head is relatively high, the fact that Namibia’s German-speaking population has a life expectancy of 79 years compared to the San’s 52 years illustrates the disparities between the different regions and ethnic groups. Being a mobile group, the San people cannot be reached with conventional service models, and the cost of reaching them is much higher than the majority of the population. Despite some progress, the group have little representation in government and often feel that they have little or no say in public health. Many distrust public health services because they are provided by other ethnic groups who may discriminate against them.
The San people could be better accounted for in public health planning if data was disaggregated by ethnicity.
Even though the tools for collecting data on ethnicity may exist, governments often do not use them, or will use the data for background information rather than using it to capture the true state of health in their country. Often this is due to limited resources, and building governments’ statistical capacity must become a major priority. Beyond this, some governments may be reluctant to break down data due to fears that doing so will enforce stereotypes or increase ethnic tensions; whilst this is an understandable fear, failing to acknowledge disparities simply reinforces a cycle of exclusion. Using data effectively to improve the health of marginalised groups could actually improve awareness of discrimination and even improve national unity, for example by countering scare-mongering by extremists.
Everyone has a role to play to make disaggregating data by ethnic group more widespread and to ensure that every woman, man and child counts regardless of ethnicity or personal circumstances. The goal for governments, development organisations, donors and international institutions must be to build up national level statistical capacity to ensure that all progress towards the new post-2015 goals is disaggregated by ethnicity and to fully account for the most marginalised indigenous and ethnic minority communities. To help combat ethnic tensions, and make progress as inclusive and effective as possible, these communities must be fully integrated into the decision making process. Only then will millions of people stop being denied their right to health.
Read Health Poverty Action's report on disaggregating data by ethnicity.
