Does COVID-19 mean we should stop worrying about chronic diseases?

By Colin Angus

The World Health Organization recently published their Global Health Estimates for 2019 with a news story that led on the fact that 7 of the top 10 causes of death worldwide are from so-called Non-Communicable Diseases (NCDs), diseases that, unlike infectious diseases, cannot be passed on from one person to the next. In recent decades huge progress has been made globally to reduce the impact of infectious diseases such as HIV, tuberculosis and malaria, but at the same time deaths from NCDs such as cancer, heart disease and diabetes have increased. In a sense this is inevitable, since we all have to die from something, but many of these deaths are premature and thus associated with a significant loss of potential years of life. Many NCD deaths can also be linked directly to behavioural risk factors such as smoking, alcohol consumption, or poor diet, or to wider social risk factors such as poverty and poor housing. As a result, there is huge potential for action on NCDs and their risk factors to improve global health.

For many people, however, their first reaction to the WHO’s headline will have been to think ‘But what about COVID?’. The best available data is that over 1.6million lives have been lost around the world to COVID-19 itself, and that is without considering the knock-on effects from overwhelmed healthcare systems and other indirect effects. Most studies which have looked at the mortality impact of the pandemic across multiple countries have found that the UK is among the worst-affected countries in the world. So, given these facts, is it time for all of us working on NCDs to retrain as infectious disease specialists? How does the burden of COVID-19 in the UK compare to the ongoing burden of NCDs?

To answer that question we can turn to the latest estimates for the Global Burden of Disease Study, led by the Institute for Health Metrics and Evaluation at the University of Washington. This study produces estimates of the number of deaths, by cause, for every country in the world, with the latest figures taking us up to 2019. Data is available for quite detailed causes of death, but let us just focus on three broad categories: NCDs, infectious diseases and injuries. For 2019, these numbers for the UK look like this:

This is a pretty striking distinction: 554,000 deaths from NCDs (89% of the total) compared to 47,000 deaths from infectious diseases (8%) and 21,000 injury deaths (3%). It isn’t hard to see from these numbers why our SIPHER Consortium was set up with a focus on NCDs. But how do these numbers compare to the death toll of the pandemic?

In the UK, conservative estimates suggest that there have been over 80,000 more deaths in 2020 compared to previous years. Using this estimate along with official figures from the Office for National Statistics, which show that 73,125 of these were confirmed as having been directly from COVID-19, we can make this comparison:

This illustrates that, even with the huge mortality impact from COVID in 2020, the total number of deaths is still dwarfed by the annual NCD burden in the UK. Even if all of the excess mortality in 2020 was due to COVID, and assuming that the ‘normal’ deaths in 2020 will look similar to 2019, infectious diseases will still only account for 18% of all deaths compared to 79% for NCDs.

Ah, but wait, some of you may be saying, maybe the people who died from COVID-19 would otherwise have died later in 2020 from an NCD, meaning that we’d expect infectious diseases to represent a larger proportion of deaths this year than this graph would suggest. That’s a reasonable hypothesis, but it just isn’t borne out by the data. Public Health England have done some excellent work to estimate how the number of deaths from non-COVID causes in England this year compares to what we would have expected to see if the pandemic hadn’t struck.

This shows that the tiny green sliver of ‘other cause’ deaths in the figure above are actual hiding some very interesting patterns. Deaths from many NCD causes are much higher than we would otherwise have expected, perhaps as a result of the huge disruption to the health service we have seen in 2020, while deaths from communicable diseases are much lower than we would have expected. The reason for this isn’t hard to figure out – all of the measures that we are taking against COVID-19 (social distancing, mask wearing, hand washing etc.) are also effective at limiting the spread of other communicable diseases. This is why the southern hemisphere saw almost no flu season this year.

Where does this leave us? Well, there is no doubt that the health impact of COVID-19 is unprecedented in scale and the pandemic requires continued collective action to try and limit further harm, at least until a vaccine has been fully rolled out and is proven to prevent serious illness and long-term COVID-related consequences in those who are protected. But, it is important that we do not lose sight of the fact that even in 2020, roughly four times as many people will die of NCDs than COVID-19 in the UK, and there is no NCD vaccination programme for us to pin our hopes on. Only concerted cross-sectoral policy efforts that prioritise health and wellbeing in all policies will be able to address the social, economic and individual burden that NCDs place on society.

Why ‘Football Manager’ is a Complex Systems Model

By Ally Brown

Before I discovered being a teenager, I played with a complex systems model every evening for hours on end, for weeks, for months. I wasn’t doing homework, it was called Championship Manager and it was all about football. It’s still going – now called Football Manager (FM) – and millions of young men, in particular, have lost themselves in the immersion of Football Manager at some point or other. And it’s a complex systems model like SIPHER is trying to build for healthy public policy. Let me try to explain.

In FM you don’t play football – it’s not like the FIFA series of games where you directly control passing and shooting. Instead you take on the role of manager of a club. You are given a squad of 15-30 players, each one with dozens of skill attributes, like pace, strength, passing and tackling, ranked from 1-20. In addition, each player has global attributes, like an age, of course, a stronger foot, a morale level, a fitness rating out of 100% that varies day-by-day, preferred positions on the pitch, variable work ethics, and preferred styles within the team. There’s endless numbers and descriptions to peruse. Some of these variables are static, others dynamic, some are quantitative, others qualitative, some are dependent and others independent; and they each impact on that player’s performance when you choose them as part of a team alongside ten other players who each have their own range of attributes.

That’s a complex system.

It’s not just complicated. If every player had quantified attributes that were stable and predictable, and the effect of combining them in a team was equally predictable, then outcomes could also be predicted very accurately. I think of the Rosetta space probe that used four gravity slingshots to travel four billion miles over ten years to land on a comet – that’s complicated, but the calculations were doable and precise. But in football, when you add in unpredictable variance (like day-to-day changes in fitness or morale); qualitative aspects (like preferred styles of play, work ethics, emotional responses); numerous interactions (the various attributes of each of 11 team members, plus substitutes, against 11 opposition players with their own arrays of attributes); and unpredictable interruptions (like injuries or refereeing decisions), then it’s not just complicated, it’s complex.

What that means in the game – just as in real football – is that while you can usually generally predict which teams are most likely to win each match, you can never do so reliably, and neither can you predict the details reliably.

In the game, as in real football, usually one of the big teams will win their leagues, but which big team wins each league varies. In the game, as in real football, there can be cup shocks, and away wins, and nil-nil draws, and sliced shots, and own goals, and hitting the post, and broken legs, and all the unpredictable things we know that contribute to the drama of football.

A famous saying about complex systems models is that “all models are wrong, but some are useful”.

In Football Manager, the same virtual football season might be played by 10,000 fans of the game. Each time the score lines and stories and wins and losses will be highly plausible. But in none of those simulations will it precisely match, in every single detail, what happens the one time the season occurs in real life. So all these ‘models’ will have been “wrong”.

But in the meantime, Football Manager is proving itself very useful, and not just for its makers who profit from its sales. Because it provides a virtual parallel world with a synthetic population of hundreds of thousands of footballers with plausible personal attributes similar to their real-life attributes, top professional football clubs now believe in Football Manager’s usefulness as a decision-making tool for scouting. In 2008 Premier League team Everton signed a deal to get deeper access to its database, and six years later the database was incorporated into an official professional scouting tool used routinely at the top level. Some players’ agents have even apparently tried to bribe the game’s makers to improve their clients’ statistics, presumably in the belief it would help them get big money moves in real life. Elite football is a multi-million-pound industry, so clubs have to get these decisions right. Football Manager is now part of their decision-making.

This is essentially what SIPHER is aiming for, but for healthy public policy. SIPHER will also have a “synthetic population” that plausibly represents real populations of people, with real health-relevant attributes. We hope that SIPHER combines useful synthetic populations with useful models of how public policy and health interact so that policymakers will trust SIPHER to inform their decisions.

Of course Football Manager doesn’t get everything right. One of the game’s makers described one such surprise in the 2001 edition as a “data error”. But perhaps Cherno Samba did not become the football superstar he was predicted to become for a difficult-to-predict, qualitative or interactive reason the game’s model simply couldn’t account for at that stage?

That’s the challenge of a complex systems model: to simulate the most plausible outcomes from all the relevant and available data. The power of the private sector has meant football has had such a model in iterative development since 1992, and SIPHER are building one for healthy public policy now. It might not be able to predict the future exactly, but its aim is to give policymakers highly plausible scenarios so as to be useful to them in improving our health, as Football Manager is useful to clubs trying to sign the next star striker.