What population trends can and cannot tell you

There is a version of this site’s argument that would be easy to write and would be wrong: “prescribing fell, deaths rose, therefore the guideline caused the deaths.” We do not make that claim, and it is worth explaining why, because the same discipline applies to claims we would like to be true.

What does the overdose data actually show?

Opioid prescribing fell substantially after 2016. Opioid-involved overdose deaths rose from 42,435 in 2016 to a peak of 82,851 in 2022, then fell to 55,007 in 2024 and a provisional 45,008 in 2025.

What that supports

One inference, and it is a strong one: whatever was driving mortality across that period was substantially not the volume of prescribing, because the volume was moving the other way. That is a claim about what the driver was not, and it is well supported.

Did the CDC opioid guideline cause the rise in overdose deaths?

National trend data cannot show that it did. Too much else changed at once — illicitly manufactured fentanyl entering the supply at scale, then becoming near-universal in it; the pandemic; changes in treatment access; changes in the population at risk. Any of those could dominate, and national aggregates cannot separate them.

It also does not support the reverse claim, that prescribing reduction was working and something else got in the way. Same problem, same data.

The 2024 fall makes the point

Deaths fell by roughly a third in two years. Naloxone distribution, changes in the composition of the illicit supply, expanded treatment, and a shrinking susceptible population are all proposed, and it is genuinely unresolved. Anyone who can confidently attribute a 27,000-death annual change to one cause could also have predicted it, and nobody did.

What evidence can show cause, not just correlation?

• Individual-level data rather than aggregates, where it exists — the tapering cohort studies are far more informative than the national series precisely because of this.
• A comparison group. Which is why pilot design insists on one.
• A dose-response relationship. The finding that faster tapers are associated with worse outcomes is more persuasive than the association itself, because gradients are harder to manufacture through confounding.
• Explicit statements of what could not be measured. The 2014 CDC evaluation of the Florida crackdown said outright that its data “did not permit any assessment of potential unintended consequences… such as reduction of access to pain medication for legitimate prescribing indications.” That sentence is worth more than most findings.

More: after the 2016 guideline.

Frequently asked questions

What is an example of correlation but not causation?

US overdose data is one. Opioid prescribing fell substantially after 2016, while opioid-involved overdose deaths rose from 42,435 in 2016 to a peak of 82,851 in 2022. The two moved at the same time, but that alone does not show the guideline caused the deaths. It does show that whatever drove mortality was substantially not the volume of prescribing, because that volume was moving the other way.

Why does correlation not prove causation?

Because too much else changes at once. Across the same years, illicitly manufactured fentanyl entered the drug supply at scale, the pandemic hit, treatment access changed, and the population at risk changed. Any of those could dominate the trend, and national totals cannot separate them. The same limit applies in reverse: the data cannot prove that cutting prescriptions was working either.

Why is causation so hard to prove?

National totals mix many causes together, so stronger evidence has to come from elsewhere. Individual-level data, such as the tapering cohort studies, is far more informative. A comparison group shows what would have happened anyway. A dose-response pattern, such as faster tapers being linked to worse outcomes, is harder to produce through confounding. Plain statements of what could not be measured matter too.