Day 287: The Long Haul

MedPage Today calls for more clarity and less credulousness over presumed COVID "long haul" cases:

First, we need clear definitions. What counts as impaired memory? Can we measure it with available scales? And, what is needed to diagnose someone with prior COVID? Some people report long COVID symptoms with negative PCR and Ab tests to SARS-CoV-2. Of course, there can be false negatives, but there can also be true negatives. How can we separate the two to better understand this problem?

A paper in Nature analyzes the sample bias in "numerous observational studies" of COVID risk factors:

Numerous studies have reported risk factors associated with COVID-19 infection and subsequent disease severity, such as age, sex, occupation, smoking and ACE-inhibitor use1,2,3,4,5,6,7,8,9,10. But to make reliable inference about the causes of infection and disease severity, it is important that the biases which induce spurious associations in observational data are understood and assessed. Bias due to confounding remains well-understood and attempts to address it are typically made (bar rare exceptions e.g. ref. 11). But the problem of collider bias (sometimes referred to as selection bias, sampling bias, ascertainment bias, Berkson’s paradox) has major implications for many published studies of COVID-19 and is seldom addressed.

A collider is most simply defined as a variable that is influenced by two other variables, for example when a risk factor and an outcome both affect the likelihood of being sampled (they “collide” in a Directed Acyclic Graph, Fig. 1a). Colliders become an issue when they are conditioned upon in analysis, as this can distort the association between the two variables influencing the collider. [...]

As illustration, consider the hypothesis that being a health worker is a risk factor for severe COVID-19 disease. Under the assumption of a higher viral load due to their occupational exposure, healthcare workers will on average experience more severe COVID-19 symptoms compared to the general population. The target population within which we wish to test this hypothesis is adults in any occupation (or unemployed); the exposure is being a health worker the outcome is COVID-19 symptom severity. The only way we can reliably estimate COVID-19 status and severity is by considering individuals who have a confirmed positive polymerase chain reaction (PCR) test for COVID-19. However, restrictions on the availability of testing especially in the early stages of the pandemic mean that the available study sample is necessarily restricted to those individuals who have been tested for active COVID-19 infection. If we take the UK as an example (until late April 2020), let us assume a simplified scenario where all tests were performed either on frontline health workers (as critical vectors for disease among high-risk individuals), or members of the general public who had symptoms severe enough to require hospitalisation (as high-risk individuals). In this testing framework, our sample of participants will have been selected for both the hypothesised risk factor (being a healthcare worker) and the outcome of interest (severe symptoms). Our sample will therefore contain all health workers who are tested regardless of their symptom severity, while only non-health workers with severe symptoms will be included. In this section of the population, health workers will therefore generally appear to have relatively low severity compared to others tested, inducing a negative association in our sample, which does not reflect the true relationship in the target population (Fig. 2b). It is clear that naive analysis using this selected sample will generate unreliable causal inference, and unreliable predictors to be applied to the general population.

The cities and towns data came out yesterday. The map colors are toned down a bit (things were getting fairly dark), and bear even less of a relationship to the grey/green/yellow/red color scheme the state has been using to declare some of us "red" since that, too, has changed. Instead of continuing to use simple case rates for the colors and just increasing the case rate cutoffs so the entire state is not red, the MDPH now does a complicated calculation involving population size, absolute case numbers, case rates, and positivity (the most nonsensical, dependent variable of all) that takes an entire page of the report to explain.

There's nothing particularly notable this week, unless it's the relatively low case rates in a couple of the Stop the Spread cities: Taunton, Randolph, and Marlborough are currently below the state average in daily case counts.

(Pop out.)

P.S. Massachusetts cases were up 1.6% today.