Air-Moving Device
@airmovingdevice.bsky.social
China and the world in data and graphs
一点浩然气 千里快哉风
airmovingdevice@protonmail.com
一点浩然气 千里快哉风
airmovingdevice@protonmail.com
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.
March 27, 2025 at 8:45 PM
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...
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.
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.
March 23, 2025 at 1:40 PM
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.
March 23, 2025 at 1:38 PM
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".
March 23, 2025 at 1:38 PM
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.
Statistically significant differences shown with Mann-Whitney and Kolmogorov-Smirnov tests (p < 1e-100).
Large cluster on very left is DC.
March 23, 2025 at 1:38 PM
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.
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.
Similar results were seen with more restricted time periods, e.g. only FY2024.
March 23, 2025 at 1:38 PM
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.
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?
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?
March 23, 2025 at 1:38 PM
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?
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...).
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...).
March 23, 2025 at 1:38 PM
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...).
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 extracted county info (FIPS code) and cross-referenced them to county-level presidential election data from 2024.
March 23, 2025 at 1:38 PM
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 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.
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.
March 23, 2025 at 1:38 PM
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.
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.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
March 23, 2025 at 1:38 PM
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.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
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.
February 18, 2025 at 6:40 PM
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.
February 18, 2025 at 6:40 PM
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.
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.
February 18, 2025 at 6:40 PM
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.
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.
Clearly, Telmisartan generics that entered jicai underwent more production-related changes than non-jicai generics.
Same trends were also seen for metformin hydrochloride generics.
February 18, 2025 at 6:40 PM
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.
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.
February 18, 2025 at 6:40 PM
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.
February 18, 2025 at 6:40 PM
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.
February 18, 2025 at 6:40 PM
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
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
February 18, 2025 at 6:40 PM
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
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
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
February 18, 2025 at 6:40 PM
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
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.
Using permit no (国药准字), drug name and manufacturer name, I matched filings to approved generics and jicai drugs.
February 18, 2025 at 6:40 PM
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.
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
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
February 18, 2025 at 6:40 PM
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
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).
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).
February 18, 2025 at 6:40 PM
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).
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).