Thread by @cap1024

Submitted (finally) revisions to this analysis of using the test-negative study design in the presence of public health measures. Some new thoughts, in light of pandemic!
Work w/ @rozeggo, @TJHladish, & John Edmunds.

NB: NOT PEER REVIEWED
medrxiv.org/content/10.110…

When criteria for testing vary systematically, the design can be biased. In outbreak & pandemic response, testing happens passively (e.g. you're sick => seek care => get tested) & actively (e.g. co-worker is test+ => contact-tracing => you get tested).

NB: NOT PEER REVIEWED

These represent diff risks and thus differ in thresholds for testing. e.g. in passive route, generic risk => testing conditional on symptoms, but for active scenario => high exposure risk => unconditional testing (e.g. COVID19 TTI protocols).

NB: NOT PEER REVIEWED

So: test-negative designs under such conditions are potentially biased. Not particularly surprising in hindsight!

What to do?

We proposed a hybrid design that deals w/ the baked-in bias, but didn't analyze its performance under other biases.

NB: NOT PEER REVIEWED

Key to that design: recording why test performed. e.g. for COVID19 control, there's testing given symptoms, testing contacts of test+, testing for travel or work, etc.

To potentially use those data in a study, however, we need *why* test was performed.

NB: NOT PEER REVIEWED

Keeping that info doesn't happen automatically - has to be planned for.

We developed this analysis (for a totally different disease, Ebola) in recognition that keeping that data wasn't a top priority. During a public health emergency, lots going on!

NB: NOT PEER REVIEWED

But we hope we've made the case for integrating this requirement into planning efforts for response activities - e.g. in data system choices, in integrated study protocols, for analytical cells in public health operations.

NB: NOT PEER REVIEWED

@threadreaderapp unroll please!