Invisible Women - When in Doubt, Blame the Algorithm

Unfortunately, I would have to say no.

What we’re seeing is — of the first 10 to 15 publications, which mostly included trials testing the drugs hydroxychloroquine and remdesivir — you’re lucky if you find out how many women and men participated in the trial. There’s no disaggregated analysis. We don’t know anything about risk factors. We don’t know anything about unwanted side effects — if there were differences or not.

I think the approach here is what unfortunately happens in an emergency — we’re trying to go as fast as possible and looking into sex differences is perceived as something that is just going to cost us time. In any case, this was what was happening before (Covid-19) as well.

It’s also a race that has financial costs. Being the first one to produce a vaccine can make or break a pharmaceutical company, especially if it’s a smaller one. And considering sex differences is simply not an issue that has a large enough lobby, which is ironic if you consider that it affects the entire population.

Failing to account for gender during the 2009 H1N1 (swine flu virus) outbreaks meant that ‘government officials tended to deal with men because they were thought to be the owners of farms, despite the fact that women often did the majority of work with animals on backyard farms’. During the 2014 Ebola outbreak in Sierra Leone, ‘initial quarantine plans ensured that women received food supplies, but did not account for water or fuel’. In Sierra Leone and other developing countries, fetching fuel and water is the job of women (and of course fuel and water are necessities of life), so until the plans were adjusted, ‘women continued to leave their houses to fetch firewood, which drove a risk of spreading infection’. (IW, p.299)

It’s not rocket science. I want to know, when it comes to effectiveness, if the curve looks the same in the female population as it does in the male one. If we look at side effects, I want to see if the numbers look the same. The problem is, when you look at all these publications, there’s always table 1 that tells you — if we’re lucky — 50% female individuals and 50% male individuals, but then if you look at the following tables and figures, you will see one population.

In the 1990s, there was a study about the effectiveness of Digoxin (a medicine for heart conditions) that was widely marketed. The first study showed that Digoxin was effective and didn’t have a higher mortality (versus placebo). And then we started to see that there seemed to be a higher incidence of side effects in women. The initial study had this nice figure with one population where you could see the curve, there was no difference between placebo and the patients who took the treatment (in terms of safety). And then a new group took exactly the same data set and analysed it according to sex. It turns out Digoxin didn’t increase mortality in men — it actually decreased mortality in men — but it increased mortality in women. And why did you not see that in the first analysis? Because there were 25% women included and 75% men.

In these Covid-19 trials I’m not asking for fancy new techniques and complicated things. I just want to see the data disaggregated at all levels so that I can see if the results are truly the same.