What does it take to make good data? That’s an important question, especially after a year of watching COVID-19 statistics being lobbed around by the minute. We need good data to see how this year has gone and to know what action to take in the future.
Unfortunately, says Dr. Paul Spiegel of Johns Hopkins University’s Center for Humanitarian Health, in lower-income countries and among certain at-risk populations, reliable data aren’t always available. So when people cite statistics, much of the world is left out or assigned inaccurate data.
Where are the gaps in data collected on the coronavirus pandemic? How does a lack of data affect fragile populations such as refugees or those in conflict areas? Spiegel, who wrote about such issues in an article published on Monday in Nature Medicine, spoke to NPR about these questions and about how wealthier countries can really be of help to low-income nations — and shared the one piece of data from the past year that really shocked him.
We have been bombarded by numbers and statistics this past year. How much of what we hear gives a true picture of what is going on globally?
In many high-income countries, where the health and data systems are better, I’m much more confident in the data. Unfortunately, in the areas where I work — fragile states, humanitarian emergencies and forced displacement settings like refugee camps and conflict areas — the data are really poor, and the anecdotal data and stories we’re seeing don’t provide a very consistent picture.
