How I Used Data Science to Double My Income In One Year

The journal Science has published a short opinion essay on the “failure” of the Carbohydrate-Insulin Model of obesity (CIM) to explain the results of half a dozen experimental tests.

This brings to at least a dozen the number of dismissive opinions in recent years, many by one or both of the same authors (Speakman & Hall), based on the same collection of weak studies and disregard of nearly a century of supportive research.

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For a description of the CIM, see this link. Below, I briefly address issues with the studies considered in the Science paper.
Animal Research

The 29-diet animal study by Speakman was strongly biased. The low-carbohydrate diets fed to the rodents contained exceedingly high amounts of saturated fat. In rodents, saturated fat causes severe inflammation and metabolic dysfunction, precluding a meaningful test of the CIM. Imagine the converse:

Suppose a high-carbohydrate diet were composed mostly of sugar, and the animals got fat. Would these authors consider that finding fair evidence in favor of the CIM? (High-carbohydrate, high-sugar diets were shown to have that effect decades ago.)

While considering this study a meaningful refutation of the CIM (in rodents), the authors neglect to mention the numerous, more fairly controlled studies that showed rapid development of obesity (in rodents) on high-glycemic index diets, as previously discussed.
Feeding Studies in Humans
Speakman & Hall feature Hall’s metabolic ward trial of 10 vs 75% carbohydrate diets, showing greater food intake on a low-carbohydrate diet. However, they neglect to point out that this effect waned over the 2-week diet period, and that there is an extensive literature on the pitfalls of such short-term studies, as summarized here.

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They also consider Hall’s 1-month ward study, touting “strict control over food intake,” but neglect to point out that the study was non-randomized and failed to control for the most basic of confounders, body weight. Due to a miscalculation, participants lost weight progressively throughout the trial. Since the low-carbohydrate diet always came second, there was again strong bias against the CIM. Even so, energy expenditure still was greater on the low-carbohydrate diet by two state-of-the-art measures, as considered here.
The authors criticize our 5-month feeding study, the longest and largest to date of this research question. They neglect to mention that the study withstood numerous published criticisms by them, and that the energy expenditure data are consistent with the dietary intake data.
Indeed, the results of our 5-month study are consistent with the longer-term research. In a new meta-analysis, we found that among short trials (≤ 2 weeks), low-carbohydrate diets decrease energy expenditure slightly. However, the longer studies — which allow the body to adapt to the change in nutrients — consistently show higher energy expenditure on low-carbohydrate diets. (I consider the issue of allowing adequate time to adapt to a low-carbohydrate diet for unbiased research here.)
Long-Term Studies
Speakman & Hall cite a 1-year study that showed no advantage to a low-carbohydrate diet, disregarding multiple meta-analysis (systematic analysis of the literature) demonstrating superiority of low-carbohydrate diets against all low-fat diet comparisons:
Straw Man Argument and Other Problems
Furthermore, the authors create a straw man argument, claiming that the CIM considers the actions of insulin only at fat cells and only the postprandial effect of insulin on fat accumulation. In the CIM, the high insulin-to-glucagon ratio elicited by modern, fast-digesting carbohydrates — and other dietary and environmental influences — affect organs throughout the body, favoring fat deposition and driving hunger. The biological actions of hormones secreted following a meal may persist for many hours. This isn’t a single hormone, single nutrient hypothesis, as previously emphasized.
Even as they criticize the CIM, Speakman & Hall fail to address the elephant in the living room:
The failure of the conventional Energy Balance Model of obesity to either prevent or treat this disorder.
Rates of obesity continue to increase worldwide, despite a primary focus for half a century on “eating less, and moving more” as the solution.
Looking Ahead
Admittedly, the CIM, like any model of a complex disease, is at best an approximation. As new data accumulate, it will need to be revised. And perhaps new models that better encompass the evidence will arise. Increased government and philanthropic funding will be needed to conduct the definitive research on this topic of great scientific and public health significance.
These unfounded “refutations” notwithstanding, the CIM remains a useful model to help guide research, with implications for designing more effective treatment in the weight loss clinic.

What this is showing is that COVID-19 death rates by age adhere almost perfectly to a log scale. In other words, there is an impressively robust exponential association between your age and your risk of death from COVID-19 — a 20 year old has a 1 in 17,000 IFR if they catch the disease, while a 60 year old is closer to 1 in 130. You can see this even more clearly in the graph using a linear scale.

In other words, based on the data we had at the time (we submitted the paper at the end of June 2020), the populations with IFR estimates seemed to lie somewhere between these values. But, importantly, it looked like age might be the biggest factor.
Fortunately, people were already working on the problem, and shortly after our first paper was published I was invited to join the effort to look at the age-stratified IFR of COVID-19. I happily accepted, bidding free time goodbye for another few months.
So what did we find?
Age-Stratified IFR
The second paper, with a bigger team including professors from Dartmouth and Harvard, and was also a bit more complex. Because the growth rate of COVID-19 scientific papers is exponential, we had a lot more data on our hands when we systematically reviewed the databases. That being said, there were far fewer places that gave good estimates of the infection rates of COVID-19 by age, which was the biggest thing we needed in a paper. Moreover, the death data was hard to trust for a lot of locations, so we limited the study only to higher-income areas where we could be reasonably sure the numbers were correct.
After aggregating more than 100 datapoints from studies across the globe, we ended up with this somewhat amazing graph.

In our initial study, we looked at something simple — based on the number of people who were estimated to have caught COVID-19 and died of it across a range of places, the aggregate IFR overall. At this point, research was pretty thin on the ground, so we looked both at people who had modelled what they thought was the likely fatality rate of COVID-19 as well as using antibody studies to infer infection rates and from those IFRs.
From these varied estimates, we produced an aggregate figure that the population IFR at the time was around 0.68% — or about 1 in 150 people who caught the disease died of it — and varied in the studies we examined between a low of 0.17% to a high of 1.7%. We also acknowledged that there was probably no ‘true’ IFR for the disease, that the number varied across populations, and in particular noted that there was growing evidence that the IFR was likely to vary based on the age-breakdown of who got infected in a population.

So, a year on, let’s look at the question of the fatality rate of COVID-19, and what we’ve learned.
Population Fatality Rates
The problem in March/April 2020 was an interesting one. While we already knew that COVID-19 was dangerous — it could overwhelm healthcare systems in a bad outbreak — there was still a lot of uncertainty. We knew that the case fatality rate, which is simply the number of deaths divided by confirmed cases, likely overestimated the true fatality risk, but we didn’t really have much information on the infection fatality rate (IFR), because we didn’t know how many people had been infected with SARS-CoV-2. While the evidence on asymptomatic spread was already accumulating, testing was sparse and so estimating exactly how many people had already had COVID-19 was very hard.

12 months on, and instead of a small side-project, the question has become something I spend most of my free time on. I’ve put in endless hours and late nights into trying to answer what at first seems like a very simple question: how likely are you to die if you catch COVID-19? In the process, I’ve published — with some amazing colleagues — two scientific papers on the question that have jointly been read 100,000s of times, cited by the CDC, WHO, EU, and others, and generally dedicated a very surprising amount of time to the whole idea.

A year ago, At the time, this was a minor side-project I could do in the extra time I had saved because I was wo