Category Archives: Epidemiology

The study of the causes and distribution of disease. A methodological branch of health sciences

Pandemic schmandemic

I was disconcerted to read that the last of the formal Pandemic Accord meetings for 2024 closed tonight (6 December 2024) without reaching an agreement. My colleague, Professor Nina Schwalbe, summed it up perfectly in her bluesky post. “Member States have missed a once-in-a-generation opportunity to make a difference because national interests prevailed over global solidarity”.

The World Health Assembly established the Intergovernmental Negotiating Body (INB) almost three years ago to “draft and negotiate a convention, agreement or other international instrument under the Constitution of the World Health Organization to strengthen pandemic prevention, preparedness and response”. When the WHA established the INB, we were in the middle of the COVID-19 pandemic. There was a visceral urgency to figure out better ways to work together globally to prevent and manage the next pandemic. Now, it’s all a bit “meh“.

In the last month, we have been gifted non-ignorable data points by the fates, which should have focused the mind. We did not need special skills to read the tea leaves at the bottom of the cup or divine the future from goat entrails.

  1. The American people re-elected Donald Trump as President of the United States and handed him a clear mandate. He campaigned on a populist America First policy and has declared (and demonstrated) an antipathy towards global treaties and accords that threaten global health.
  2. Trump also announced that Robert F. Kennedy Jr. (RFK Jr), a vaccine denier, would be the Health Secretary. RFK Jr is also on record that there is too much focus on infectious diseases.

Together, these will create geopolitical friction in negotiating a pandemic accord that may be impossible to overcome. Fate has also been teasing us with news of infectious diseases among those geopolitical tea leaves.

  1. A mystery infectious disease has appeared in a remote area of the Democratic Republic of Congo. According to the Ministry of Public Health, there have been 394 cases and 30 deaths.
  2. Influenza A subtype H5N1 is the stuff of infectious disease specialists’ nightmares. It has a very high case fatality rate–typical ‘flu’ has a fatality rate of <1%. H5N1 has a case fatality rate of around 50%. The saving grace has been that it had not adapted to human-to-human transmission. Human transmission might be about to change. It has swept through U.S. dairy herds and is found in raw milk. Did I mention that RFK Jr. is a fan of raw milk?

This failure is particularly bitter because they’re walking away from the negotiating table when the stars are aligning for potential future crises. We have a new U.S. administration openly sceptical of global health cooperation, an increasingly complex geopolitical landscape, and emerging pathogens testing our surveillance and response capabilities. The window of opportunity that opened during COVID-19–when the world’s attention was focused on pandemic preparedness–appears to be rapidly closing.

Local causation and implementation science

If you want to move a successful intervention from here (where it was first identified) to there (a plurality of new settings), spend your time understanding the context of the intervention. Understand the context of success. Implementation Science—the science of moving successful interventions from here to there—assumes a real (in the world effect) that can be generalised to new settings. In our latest (open access) article, recently published in Social Science and Medicine, we re-imagine that presumption.

As researchers and development specialists, we are taught to focus on causes as singular things: A causes B. Intervention A reduces infant mortality (B1), increases crop yields (B2), keeps girls in school longer (B3), or…. When we discover the new intervention that will improve the lives of the many, we naturally get excited. We want to implement it everywhere. And yet, the new intervention so often fails in new settings. It isn’t as effective as advertised and/or it’s more expensive. The intervention simply does not scale-up and potentially results in harm. Effort and resources are diverted from those things that already work better there to implement the new intervention, which showed so much promise in the original setting, here.

The intervention does not fail in new settings because the cause-effect never existed. It fails in new settings because causes are local. The effect that was observed here was not caused by A alone. The intervention was not a singular cause. A causes B within a context that allows the relationship between cause and effect to be manifest. The original research in which A was identified had social, economic, cultural, political, environmental, and physical properties. Some of those properties are required for the realisation of the cause-effect. This means that generalisation is really about re-engineeering context. We need to make sure the target settings have the the right contextual factors in place for the intervention to work. We are re-creating local contexts. The implementation problem is one of understanding the re-engineering that is required.

 

What is the optimal number of broken jaws?

I was chatting with a friend recently about the COVID-19 response in different countries. Reflecting on her own country, she said, “It is so hard to know what is right!”; that is, it is so hard to know what the right response to COVID-19 should be.

The variation, for instance, in countries’ lockdown responses is substantial, but which country is doing the right thing? In some countries, there has been no lockdown. The government asked the people to be sensible. In other countries, the government legally confined people to their homes — only one person was allowed out at very specific (restricted) times to buy essentials. Given these two policy extremes (be sensible and house arrest), which one is the right one, and how do you know?

An economist, I have forgotten who once asked tongue-in-cheek, what is the optimal number of dead babies? The very purpose of such a crass question is to make you stop and think. What tradeoffs are you prepared to make to save the lives of babies? Sure, you could be lazy, condemn the questioner as immoral (for even asking you to think), and declare zero dead babies to be the right number. As a simple policy proposition, if zero dead babies is the right number, then all the resources of society should be aimed at preventing neonatal deaths. ALL RESOURCES! Until the policy goal has been achieved, there is more work to be done to reduce the number. One dead baby is too many!!! Farmers may farm, but only to produce the food that supports the workforce that is striving to reduce baby deaths to zero. Teachers may teach, but only to educate the people to fill the jobs to support the policy goal to reduce baby deaths to zero. There is very limited use for art, music, cinema, sport, fashion, restaurants, etc. They will all have to go! If five-year-old deaths increase, that is something to live with, just as long as we can save another baby.

At this point, you’re probably thinking, well that’s stupid. That’s not what I meant when I said the optimal number of dead babies is zero. What I meant was something more along the lines of, “In an ideal world there would be zero dead babies”. Equally, if you were asked about poverty or crime, or amazing works of art, you presumably would have stated the ideals in terms of zero poverty, zero crime, and lots more wonderful art. And this is quite a different proposition. An ideal world is not ideal in virtue of its achievement of a single goal. It is ideal in having achieved all sorts of different outcomes. And that is why the real and the ideal do not intersect. In the real world, we do not achieve the ideal anything. We seek to achieve many ideals, and realistically, we hope to make progress against them, knowing that there is always more to be done. In striving to improve the societal position against a basket of goals, we allocate limited resources and make trade-offs.

This is one part of the COVID-19 problem, and, as my friend observed, why it is so hard to know what is right. What is the right number of COVID-19 deaths? There are lots of important, rational debates to be had around this topic because it is about the tradeoffs we are prepared to make against a basket of societal goals against the myopic achievement of one. Muscular public health responses — effective house arrest — are very good at reducing the number of new COVID-19 cases. They are also very effective at increasing domestic violence, increasing depression, lowering child immunisation rates, degrading child education, increasing poverty and increasing unemployment. If the societal goal should be zero COVID-19 deaths, what is the optimal number of broken jaws, suicide attempts, measles encephalitis cases, illiterate and enumerate children, beggars, and soup kitchens?

All these issues, under normal circumstances, are things of concern to Public Health and maybe, one day, they will be again.

Another part of the COVID-19 problem is that, whether a government “did the right thing” will be determined in hindsight, and by making (inadequate) historical comparisons between the outcomes across countries’. In democracies, at least in the short-term, “did the government do the right thing?” will often be decided at the ballot box. This will surely get the answer wrong. In less-than-democracies, astute rulers will write the history books themselves ensuring that, without regard to the outcome, the government did the right thing.

One of the main reasons that “it is so hard to know what is right!” is that we rarely have a societal view about the long term goals we wish to achieve and the tradeoffs we are prepared to make. Furthermore, we are reluctant to accept the fact that one can do the right thing and still fail. We assume that the right course of action will, by definition, result in success. We are prospective Kantians and retrospective Utilitarians.

Donald Trump’s BMI: getting the measure of the man.

I find myself fascinated by a pointless lie because it is inescapably tragic. All it can do is diminish the person in the eyes of others. And this brings us to Donald Trump’s height. In January 2018, the Physician to the President, Ronny L. Jackson MD asserted that Donald Trump was 6’3″ tall (1.90m). This is so unlikely to be true, that it stretches credulity. There is no reason for Jackson to lie spontaneously about a patient’s height, and it seems probable that he was encouraged to add a few inches by the President himself.

When asked to self report height both men and women in the US tend to overstate it.  Burke and Carman have suggested that overstating height is motivated by social desirability — you can never be too tall. There is ample evidence of Donald Trump’s (misplaced) search for the socially desirable with respect to his hair, his tan, his ethnicity, his intelligence and now his height.

In 2018 we learnt that Donald Trump was officially not quite Obese (body mass index (BMI) <30), and in 2019 he had nudged over the line into the obese range (BMI ≥30). Overstating height creates a problem in the calculation of BMI — which is mass (in kilograms) divided by height (in meters squared). Given that Donald Trump is likely shorter than 1.9m (6’3″), and probably closer to 1.854m (6’1″) this will have implications for whether he was really obese in 2018 (not just overweight as stated by his Physician) and just how obese he probably is (Figure 1).

Figure 1: Donald Trump’s BMI in 2018 and 2019 given different assumptions about his height [R-code here].

In 2018 Donald trump was just below the obese category if and only if he was really 6’3″ (1.9m) tall.  At any height less than that he was obese in 2018 and he is obese today.  His most likely true height given comparisons with others (cf, Barack Obama) is 6’1″, and this puts him comfortably in the obese range.

Misrepresenting one’s height does not create a problem if the lie is reserved for others — except perhaps in a political sense. Problems arise if one deludes oneself. Telling others that you are taller and healthier than you really are is one thing; if you lie to yourself you cannot properly manage your health.