Covid-19 Leading and Lagging Measures

As Covid-19 exploded into our lives, many of us became hungry for data, hoping to get an insight into the developing crisis.  In the trail of the terrible virus came a torrent of opinion, data and ‘research’ of varying quality. Despite this, clear truths emerged and I realised that there were some very clear leading and lagging measures.

Leading and Lagging?

If you’re not familiar, a leading measure is a predictive measure of an outcome, it looks forward. A lagging measure looks backwards to see if a result was achieved.

The example I use in my training concerns your weight, a lagging measure, with two obvious leading measures. Those leading measures are the number of calories you consume and the amount of calories you burn in exercise will predict your future weight.

In my experience defining high quality leading measures is one of the most challenging elements of goal setting. It’s a skill.

Applying it to Covid-19 

Returning to Covid-19, healthcare is arguably the most complex, multi-agency, multi-disciplinary outcomes driven industry on Earth. It also has an enormous range of inputs and metrics or leading measures.

Think about all the things you do that might impact your health. What and when you eat, your exercise, drink, whether you stand or sit, smoking, stress. These are all leading measures to your personal health outcome. Complex and hopefully very lagging!

With a bit of time on my hands and a minor obsession with outcomes thinking, I started to see the plans for Covid-19 as objectives and key results, with well defined lead measures.

The objective is the most laudable of all, save lives. To this end our main key result is minimising deaths from Covid-19 and from overloaded health systems. There is also a balancing economic metric to ensure the cure isn’t worse than the disease. This.

At the speed the virus travels these are lagging metrics, but we are aware of the leading measures, they are played out in public on a daily basis. They are based on the hypotheses that flattening the curve of cases will give the health services the opportunity to save lives without becoming overwhelmed.

Here are the leading measures for reducing the impact that I’ve identified:

  • Concurrent cases of Covid-19: a flatter curve reduces the load on healthcare systems.
  • ICU beds: which increases the capacity of the healthcare system to treat patients
  • Ventilators: to treat the most severely ill patients
  • Number of available healthcare professionals:to treat the patients. Arguably availability of PPE is a further lead measure for this.
  • Days of lockdown: the longer the lockdown the bigger the impact on GDP. Planning to end the lockdown safely will be one of the most complicated elements of this deadly puzzle.

Just like the OKRs we use in business, there are activities and hypotheses to help achieve these goals. Here is an example subset of the metrics and activities for increasing the medical capacity.

Although we don’t see the daily ebb and flow of these leading measures, they will be a matter of life and death.