Impact of the COVID-19 Pandemic on Medical Expenditures among Medicare Fee-For Service Beneficiaries Aged 67 Years or Older With Diabetes
Bibliographic record
Abstract
Objective: To compare total and out-of-pocket (OOP) medical expenditures between pre-COVID-19 (03/2019—02/2020) and COVID-19 (03/2020—02/2022) periods among Medicare beneficiaries with diabetes. Research Design and Methods: Data were from 100% Medicare fee-for-service claims. Diabetes was identified using the International Classification of Disease codes 10th revision. We calculated quarterly total and OOP medical expenditures at population and per capita level in total and by service type. Per capita expenditures were calculated by dividing the population expenditure by the number of beneficiaries with diabetes in the same quarter. Changes in expenditures were calculated as the differences in the same quarters between the pre-pandemic and pandemic years. Results: Population total expenditure fell to $33.6 billion in the first quarter of the pandemic from $41.7 billion in the same pre-pandemic quarter; it then bounced back to $36.8 billion by fourth quarter of the second pandemic year. The per capita total expenditure fell to $5,356 in the first quarter of the pandemic from $6,500 in the same pre-pandemic quarter. It then increased to $6,096 by the fourth quarter of the second pandemic year, surpassing the same quarter in the pre-pandemic year ($5,982). Both population and per capita OOP expenditures during the pandemic period were lower than the pre-pandemic period. Changes in per capita expenditure between the pre-COVID-19 and COVID-19 periods by service type varied. Conclusions: COVID-19 had a significant impact on both total and per capita medical expenditures among Medicare beneficiaries with diabetes. The COVID-19 pandemic was associated with a lower OOP expenditures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".