What election surprises can teach us about global health metrics


Donald Trump’s victory took the world by surprise this week. Like Brexit, most commentators, polls and election modellers got it wrong.

Understanding what went wrong will be much debated over the coming weeks. It also contains valuable lessons for those who use and study global health measures.

There are many possible reasons why most polls predicted election results incorrectly. Have many people changed their minds? Did people lie about their intentions to vote for Trump, as the ‘shy Trump’ theory suggests Marlet reported this week? Have Trump voters disproportionately refused to talk to pollsters? An idea discussed in the FiveThirtyEight.com Election Podcast was that the pollsters’ assumptions about nonrespondents were incorrect.

To better understand what global health professionals should take away from this election upset, I sat down with Institute of Health Metrics and Evaluation Professor Theo Vos, a key member of the Global Burden of Disease research team at IHME.

“For the Global Burden of Disease study, we spend a lot of time understanding what systematic bias is in different data sources,” Vos said.

For example, in countries where civil registration (death certificate) data is not available, it is common to conduct household surveys to measure adult deaths. Investigators are asking people about deceased siblings. Scientists have show that these surveys underestimate the number of adult deaths for two main reasons. First, people do not always remember deaths (recall bias). Second, households with many deaths tend to break up, so surveys are likely to miss them (selection bias).

Biased data also appears to have been a major challenge for pollsters and election modellers. There may have been a big difference between the people who took the polls and those who showed up to vote on Election Day.

According to Vos, another lesson learned from the election was that “you have to do the right thing. Poll data is far from the gold standard for what you actually want to measure. »

As Election Day wore on and polling stations began to close, Vos pointed out, “[The election modelers] got much better information and their predictions improved.

Although the improved forecasts came late, there is an important takeaway here for global health metrics: collection and access to high-quality data is key to improving estimates.

“We regularly call on researchers to use the best quality methods to collect data,” Vos said. He cited several examples where researchers have come together to build consensus on the best ways to measure certain health outcomes. “Multiple Indicator Cluster Surveys (MICS) and Demographic and health surveys (DHS) are fantastic examples.

MICS and DHS provide comparable population and health data for many less developed countries.

Like predicting election results, measuring global health is a complex task and a big responsibility. To improve health, researchers need to get the estimates right. The international community must continue to raise the bar for scientific excellence in health metrics and advocate for better data collection.

David Phillips of the Institute for Health Metrics and Evaluation contributed to this article.


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