From the article:
As predicted, studies with younger cohorts and separating former and occasional drinkers from abstainers estimated similar mortality risk for low-volume drinkers (RR = 0.98, 95% CI [0.87, 1.11]) as abstainers. Studies not meeting these quality criteria estimated significantly lower risk for low-volume drinkers (RR = 0.84, [0.79, 0.89]). In exploratory analyses, studies controlling for smoking and/or socioeconomic status had significantly reduced mortality risks for low-volume drinkers. However, mean RR estimates for low-volume drinkers in nonsmoking cohorts were above 1.0 (RR = 1.16, [0.91, 1.41]).
Studies with life-time selection biases may create misleading positive health associations. These biases pervade the field of alcohol epidemiology and can confuse communications about health risks. Future research should investigate whether smoking status mediates, moderates, or confounds alcohol-mortality risk relationships.
This is really interesting, but I got a bit confused by the language.
Can someone explain, what the “common mistake” being done by bad research until now is?
And what is the conclusion relating to alcohol use?
What they see as “bad research” is looking at an older cohort without taking into consideration their earlier drinking habits - that is, were they previously alcoholics or did they generally have other problems with their health?
If you don’t correct for these things, you might find that people who are not drinking seems less healthy than people who are. BUT, that’s not because they’re not drinking, it’s just because of their preexisting conditions. Their peers who are drinking a little bit tend to not have these preexisting conditions (on average)
People that don’t drink used to drink a lot or had other conditions that stopped them from drinking. This wasn’t taken into account when creating studies about it not drinking, and low drinking where done. This made it look like low drinkers fared better than non-drinkers. But non-drinkers were non-drinkers for an unhealthy reason. And that’s why we ended up with studies that said some drinking is healthier than none. But it turns out they were wrong. Because the non-drinkers they used had medical issues that prevented them from drinking and caused more problems.
As I understand it, some studies don’t distinguish low-volume drinking from not drinking. Grouping non-drinkers with low-volume drinkers will bias that group to look healthier.
In addition to this, some studies don’t include younger individuals. I suppose this biases the study outcomes towards older-age related effects while neglecting younger-age effects. An example of an older-age effect could be an interaction between alcohol and a blood thinning medication or heart disease. An example of a younger-age effect could be excessive drinking combined with behaving like a complete buffoon
The quoted portion of the meta-study in the post makes it clear that the studies reviewed did distinguish between low-volume drinking and not drinking.
It wouldn’t surprise me if sample selection not taking into account social factors which would cause people who drink at low volumes to lie and say they don’t drink could play a role in certain studies.
They seemingly tend to crop out gender, socio-economic standings, and ethnicity all of which can alter the data. Although they’re not used I believe due to scope and variability. It is also noted there are external influences which could in theory “fudge” the data. At least as far as I see it. There also seems to be a tie to smoking and alcohol which perhaps links the two risky behaviors together. An abstaining of one, leading to an abstaining in the other. Which leads in part to what others are saying about other health issues not being tracked or noted.
But ultimately I would say addiction, as a disease, is quite abstract. And it’s very social in nature. And that many cultures spread it through coming-of-age rituals. Which makes it difficult to villainize. But also difficult to “pin down” when an activity reaches an unhealthy level. Also people lie. So all of this could lead to fudged data as well.