Why do GBD researchers bother to use new and potentially unfamiliar metrics instead of tried-and-true, older ways of discussing disease, like prevalence and incidence? To illustrate how GBD metrics complement other population health metrics, let’s consider some standard public health metrics, and how GBD-specific metrics build on them.
Every day we encounter and use mathematical models. From producing weather predictions for the week, to calculating a country’s GDP, to estimating the impact of vaccinations, models help us process, represent, and understand the data that describe the workings of the world around us.
We use more 90,000 data sources in the Global Burden of Disease. Why do we use estimates instead of simply presenting the data points?
Knowing what someone died of can be complicated. We often talk and think about death as a singular event. We say, “he died of cancer” or “she died of old age.” In reality, a series of domino effects are often occurring inside the body that lead to someone’s death.
The Global Burden of Disease (GBD) study relies on a lot of data – over 90,000 data sources, in fact. Each of these data sources has their own distinct way of collecting information and measuring health. How do we make these sources speak the same language?
Estimates are only as strong as the evidence they are built on. The Global Burden of Disease (GBD) study produces millions of estimates of health around the globe, estimates that are informing real-world policy and implementation. That means that they have to be built on good data, and a lot of it.
Everyone deserves to live a long life in full health. Inspired and fueled by this idea, the Global Burden of Disease study, or GBD, seeks to answer the question of what sickens and kills people of all ages around the world.
What do the largest development bank, largest global public health agency, and largest funder of primary biomedical research have in common? Well, among other things, their use of IHME’s work for decision-making.