Influence of the COVID-19 Pandemic on Overall Physician Visits and Telemedicine Use Among Patients With Type 1 or Type 2 Diabetes in Japan
Bibliographic record
Abstract
BACKGROUND: Regular visits with healthcare professionals are important for preventing serious complications in patients with diabetes. The purpose of this retrospective cohort study was to clarify whether there was any suppression of physician visits among patients with diabetes during the spread of the novel coronavirus 2019 (COVID-19) in Japan and to assess whether telemedicine contributed to continued visits. METHODS: We used the JMDC Claims database, which contains the monthly claims reported from July 2018 to May 2020 and included 4,595 (type 1) and 123,686 (type 2) patients with diabetes. Using a difference-in-differences analysis, we estimated the changes in the monthly numbers of physician visits or telemedicine per 100 patients in April and May 2020 compared with the same months in 2019. RESULTS: For patients with type 1 diabetes, the estimates for total overall physician visits were -2.53 (95% confidence interval [CI], -4.63 to 0.44) in April and -8.80 (95% CI, -10.85 to -6.74) in May; those for telemedicine visits were 0.71 (95% CI, 0.47-0.96) in April and 0.54 (95% CI, 0.32-0.76) in May. For patients with type 2 diabetes, the estimates for overall physician visits were -2.50 (95% CI, -2.95 to -2.04) in April and -3.74 (95% CI, -4.16 to -3.32) in May; those for telemedicine visits were 1.13 (95% CI, 1.07-1.20) in April and 0.73 (95% CI, 0.68-0.78) in May. CONCLUSION: The COVID-19 pandemic was associated with suppression of physician visits and a slight increase in the utilization of telemedicine among patients with diabetes during April and May 2020.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".