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I was being a bit glib/imprecise before, but I'm specifically talking about the Northwestern Memorial campus downtown Chicago [0]. That location qualifies for 340B as a Rural Referral Center (RRC), and got itself reclassified by CMS/HRSA as rural to do so, despite being in the middle of downtown. RRCs need to meet a lower threshold of Disproportionate Share Hospital (DSH) adjustment percentage (8% vs the usual 11.75%). Northwestern Memorial needs to be an RRC because it doesn't meet the higher DSH threshold.

AFAIK, the other hospitals/clinics under the Northwestern umbrella don't really factor into whether the downtown Northwestern Memorial campus qualifies for 340B (insofar as they all have their own CCNs and qualify independently). In this case, Northwestern Memorial qualifies because it a) got reclassified as rural b) became an RRC (likely based on its staff specialty mix) c) meets the RRC DSH threshold of >= 8%.

Northwestern Memorial does treat a lot of rural patients, so maybe it does deserve 340B. That said, it seems clear that it's not they type of struggling safety-net/rural hospital 340B was originally intended to subsidize.

[0] https://340bopais.hrsa.gov/CeDetails/78783

Thanks! Good to see other health policy wonks on HN

Hi, I wrote this article and largely agree with you. 340B is important and without it many hospitals likely wouldn't survive. However, it's pretty evident at this point that 340B has expanded beyond its original intent.

For example, Northwestern University (in the middle of downtown Chicago) got itself reclassified as a rural hospital in order to participate in the program.

Moreover, it's grown extremely rapidly over the past ~5 years, and the gravity of the program is starting to create bizarre second-order effects like the one outlined.

My intent with this article is just to highlight some of those effects, not to advocate for eliminating 340B.

Also, not bankrolled by pharma, just a researcher for Turquoise Health (a healthtech startup). I get to dig around in their data and publish occasionally, but editorial control / opinions are my own.

I actually tried a couple different engines before landing on OSRM. I started with R5 (since it can also do public transit) then switched to Valhalla.

The main limiting factor was speed. Basically all routing engines except for OSRM are too slow to compute continent-scale travel time matrices. For reference, it took Valhalla around a week to finish 3 billion point-to-point pairs. OSRM did the same calculation in about 2 hours.

I can't speak to Graphhopper since I haven't tried it. Maybe something to test in the future!

These are great suggestions, thank you!

I played around with a single static colormap for all scales but couldn't find one that worked well/looked good. Perhaps I'll add a slider that lets you manually select the values of the legend.

The second suggestion is a no-brainer. I'll definitely get that added.

Thanks! I hadn't seen anyone do it this way before with a very large, partitioned dataset, but it works shockingly well as long as you're not trying to `SELECT *` the entire table. Props to the DuckDB folks.

Eventually I plan to add some thin R and Python wrapper packages around the DuckDB calls just to make it easier for researchers.

Good point, will do!

It's just using OpenStreetMap tags for routing, so if a bridge is impassible by foot/bike according to OSM, then it won't be able to route there. See the Verrazano-Narrows Bridge in New York as an example.

In the next year or so maybe. The biggest obstacles to adding public transit are:

- Collecting all the necessary scheduling data (e.g. GTFS feeds) for every transit system in the county. Not insurmountable since there are services that do this currently.

- Finding a routing engine that can compute nation-scale travel time matrices quickly. Currently, the two fastest open-source engines I've tried (OSRM and Valhalla) don't support public transit for matrix calculations and the engines that do support public transit (R5, OpenTripPlanner, etc.) are too slow.