Air-Moving Device

You have a great point! — re if most grants are to universities then that’s the hidden variable. The majority of grants/contracts are not to universities, and I see similar results when excluding universities from the analysis.

Relevant analysis: bsky.app/profile/airm...

It is therefore possible that they made cancellations unbiasedly across the Trump-Harris political spectrum but preferentially disclosed ones to Harris counties for publicity purposes.

Potential caveat: DOGE doesn't specify how it chose certain contract/grant cancellations to disclose. They claim the ones disclosed represent "~30% of total savings".

Clearly, the background/control sets are distributed across the Trump-Harris spectrum, but the cancellations are biased towards Harris counties.

Statistically significant differences shown with Mann-Whitney and Kolmogorov-Smirnov tests (p < 1e-100).

Large cluster on very left is DC.

To answer this, I need a good background/control set. I compiled all contracts/grants from FY2021-2025 on USAspending, totaling ~19M/24M. ~99% of all cancelled contracts/grants were from this period.

Similar results were seen with more restricted time periods, e.g. only FY2024.

I plotted every cancellation, with total dollar amount obligated on the y axis against Trump-over-Harris margin on x.

Clearly, there's a bias for more cancellations in Harris counties. But does this reflect true bias or simply more contracts/grants awarded to Harris counties?

I used election data scraped from Fox News (www.foxnews.com/elections/20...) by github.com/tonmcg/US_Co...

For each contract/grant, I found Trump's popular vote margin over Harris in the recipient county.

Similar results were seen with NYT's election data (github.com/nytimes/pres...).

These metadata include total dollar amounts obligated, dates, and information on contract/grant recipients (address, county, congressional district, etc).

I extracted county info (FIPS code) and cross-referenced them to county-level presidential election data from 2024.

I retrieved all publicly available cancellations from DOGE on 3/22, which according to DOGE is a subset of all cancellations.

I then cross-referenced them to official spending data on USAspending using links provided by DOGE and ended up with 5,137 and 4,679 contracts and grants with rich metadata.

Data source:
doge.gov/savings — cancelled federal grants and contracts
USAspending.gov — contract/grant recipient info
github.com/tonmcg/US_Co... & github.com/nytimes/pres... — county-level election data

DOGE/Musk preferentially cancelled grants and contracts to recipients in counties that voted for Harris in 2024.

Among cancellations with election data available, 92.9% and 86.1% cancelled grants and contracts went to Harris counties, representing 96.6% and 92.4% of total dollar amounts.

Again, I’m in no way against centralized volume-based procurement and generics. In fact, I think they’re fundamentally a great idea for patients, given that their safety and effectiveness are demonstrated with rigorous testing and proper regulatory oversight.

While these production-related changes do not necessarily affect drug efficacy or safety, NMPA does not disclose the regulatory tests and inspections done (if any) that addresses whether new suppliers, processes, or sites materially impact drug composition or performance.

Expanding this to the entire dataset, I found 121 drugs that had both jicai and non-jicai generics, n = 352 and 768 respectively.

While the number of total filings were similar between the two groups, production-related changes in jicai drugs were ~2-fold that of non-jicai.

Here I am plotting the cumulative number of changes, averaged by the number of drugs.
Clearly, Telmisartan generics that entered jicai underwent more production-related changes than non-jicai generics.

Same trends were also seen for metformin hydrochloride generics.

For example, there are a total of 28 Telmisartan generics that passed BE, and 7 of these entered jicai on 2021/2/8. I tabulated the number of production-related changes (supplier, process, or site) that happened for jicai vs non-jicai drugs starting on 2021/2/8.

To test this more rigorously, I compared jicai drugs with drugs that (1) share the same active ingredient and (2) passed BE tests, but (3) did not enter jicai.

An interesting finding here is that percentages of drugs that underwent postapproval changes are higher for jicai drugs than generics. A hypothesis is that jicai drugs undergo more postapproval changes due to cost pressures associated with low bids.

Drugs that entered jicai:
Here I plotted for each drug the date it entered jicai and dates of all supplemental filings.

* 45.7% of jicai drugs changed suppliers post-approval
* 16.4% changed production processes
* 15.3% changed manufacturing sites

Generics that passed BE:
Here I plotted for each drug the date it passed BE and dates of all supplemental filings.

* 28.2% of generics changed suppliers post-approval
* 9.6% changed production processes
* 14.1% changed manufacturing sites

I focused on 3 types of changes: supplier, production process, and manufacturing site. These are more likely to impact drug efficacy than other changes.

Using permit no (国药准字), drug name and manufacturer name, I matched filings to approved generics and jicai drugs.

Data source--

Supplemental filings:
www.nmpa.gov.cn/datasearch/s...

Generics that passed BE:
www.cde.org.cn

Jicai drugs: Shanghai division of NHSA
www.smpaa.cn

I analyzed supplemental filings disclosed by NMPA during 2019/1/1-2025/2/5 and found a total of >160k filings.

I cross referenced these with generics that passed BE disclosed by NMPA (n = 1,988) and drugs that entered centralized procurement jicai disclosed by NHSA (n = 1,933).

After passing bioequivalence tests, generics can undergo changes in supplier, manufacturing process or site. These are often submitted as supplemental filings to province-level drug admins, and no further BE tests are required.

Here I quantified how prevalent these changes are.

Thread: prevalence of post-approval changes in generic drugs and jicai (集采) drugs.

I analyzed >160k supplemental filings and found widespread post-approval changes in generics and jicai drugs. Importantly, jicai drugs underwent more changes than non-jicai counterparts.

bsky.app/profile/airm...

一致性评价是确保仿制药有效、安全的关键监管措施。然而，若仿制药在通过一致性评价后，生产环节发生变化，是否依旧符合与参比试剂的一致性？

实际上，一款仿制药在通过一致性评价后，可对原材料供应商、生产工艺、生产厂址等多项生产环节进行变更，而无需重新进行一致性评价，多数情况只需在省级药监部门进行备案。

我分析了国家药监局公布的2019年至今的16万余条药物补充备案，发现通过一致性评价的仿制药、进入集采的药品中，广泛存在过评后生产环节的变更。

并且，进入集采的药品，相对于同成分但未进入集采的药品，进行了更多此类变更。

这些变更并非一定会影响药效、安全性，但仍需解决如何对此进行有效监管的问题。

比较干净的药品销量数据可能得靠一些商业数据库了，好像没有什么很好的公开数据

推广仿制药、集采制度是解决医保支付问题、为广大患者提供低价药品的必要的、有效的方法。

为应对、缓解患者和广大民众对仿制药和集采制度的质疑，药厂、药监部门、医保部门应扩大信息公开的力度并确保公开的信息及时准确。药监部门也应对于可能影响药效、安全性的过评后变更进行更加有效的审核与监管。

需要指出的是，进行了供应商、生产工艺、生产厂址变更，并非一定会对药物成分、药效造成影响。

但绝大多数非业内人士对于过评药品、集采药品可能会在过评后进行生产变更并不知情。

药监部门也并未披露关于这些变更的详细信息——如厂家对新供应商的原料进行杂质分析的情况、药监部门是否对新增的生产厂址进行检查、采用新工艺生产的药品是否进行了体外生物等效性分析等等。