“What gets measured gets done.” It’s an expression often cited by global health advocates, notes World Health Organization official Erin Kenney. And she says it’s the reason she’s so encouraged by WHO’s unveiling this week of a sweeping new database highlighting health inequalities that need addressing.
Billed as the world’s most comprehensive collection of statistics on the topic, the Health Inequality Data Repository allows users to compare how people of differing incomes, ages, genders and rural-versus-urban settings compare on more than 2,000 measures of health, ranging from access to key health services to child mortality rates – and even upload and analyze their own data.
“It’s critical for us to look at various dimensions of inequality … to truly understand who’s being left behind in different contexts,” said Kenney, acting director of WHO’s Department of Gender, Diversity Equity and Human Rights.
She was speaking at a press conference to demonstrate how the new database works. Here are three takeaways from that event.
There’s a gaping need for more data on health inequality.
The new WHO database is unquestionably extensive, featuring 11 million data points pulled from 15 global statistics sources. Yet health officials at the press conference mainly stressed how much information was still missing from it.
This is partly due to gaps in the collection of health statistics more generally. “Nine out of 10 deaths in Africa are not reported,” noted Samira Asma, WHO’s assistant director general in charge of data analytics. “Imagine the huge missed opportunities to bridge inequality gaps.”
Data collectors are especially likely to miss people who belong to vulnerable groups, said Francesca Perucci, assistant director of the United Nations’ Statistical Division. “For example,” she said, “a survey of national statistical offices found that 39% could not adequately collect data on migrants, 27% had difficulties collecting data on older persons and 27% had challenges with data on persons with disabilities.”
When health statistics are available, they are often still not broken out by all the relevant sub-categories. That’s a problem because statistics that average out measures of well-being can paint a misleading picture – hiding pockets of extreme suffering.