Impact of the COVID-19 Pandemic on Primary Care Access for Patients with Hematologic Malignancies
Bibliographic record
Abstract
Abstract Introduction: Primary care physicians are essential to cancer care. They frequently identify signs and symptoms leading to a diagnosis of cancer, and provide ongoing support and management of non-cancer health conditions during cancer treatment. Both primary care and cancer care have been greatly affected by the COVID-19 pandemic. In the United States, cancer-related patient encounters and cancer screening decreased over 40% and 80% respectively in January to April 2020 compared to 2019 (London et al. JCO Clin Cancer Inform 2020). However, the impact of the COVID-19 pandemic on primary care access for cancer patients remain unclear. Methods: We undertook a population-based, retrospective cohort study using healthcare databases held at ICES in Ontario, Canada. Patients with a new lymphoid or myeloid malignancy diagnosed within the year prior to the COVID-19 pandemic, between July 1, 2019 and September 30, 2019 (COVID-19 cohort) were compared to patients diagnosed in years unaffected by the COVID-19 pandemic, between July 1, 2018 - September 30, 2018 and July 1, 2017 - September 30, 2017 (pre-pandemic cohort). Both groups were followed for 12 months after initial cancer diagnosis. In the COVID-19 cohort, this allowed for at least 4 months of follow-up data occurring during the COVID-19 pandemic. The primary outcome was number of in-person and virtual visits with a primary care physician. Secondary outcomes of interest included number of in-person and virtual visits with a hematologist, number of visits to the emergency department (ED), and number of unplanned hospitalizations. Outcomes, reported as crude rates per 1000 person-months, were compared between the COVID-19 and pre-pandemic cohorts using Poisson regression modelling. Results: We identified 2882 individuals diagnosed with a new lymphoid or myeloid malignancy during the defined COVID-19 timeframe and compared them to 5997 individuals diagnosed during the defined pre-pandemic timeframe. The crude rate of in-person primary care visits per 1000 person-months significantly decreased from 574.4 [95% CI 568.5 - 580.4] in the pre-pandemic cohort to 402.5 [395.3 - 409.7] in the COVID-19 cohort (p < 0.0001). Telemedicine visits to primary care significantly increased from 5.3 [4.8 - 5.9] to 173.0 [168.4 - 177.8] (p < 0.0001). The rate of combined in-person and telemedicine visits to primary care did not change from 579.8 [573.8 - 585.8] in the pre-pandemic cohort to 575.5 [566.9 - 584.2] in the COVID-19 cohort (p = 0.43). In-person visits to hematologists decreased from 504.1 [498.5 - 509.7] to 432.8 [425.3 - 440.3] (p < 0.0001), and telemedicine visits to hematologists increased from 6.6 [6.0 - 7.3] to 75.9 [72.8 - 79.1] (p < 0.0001). The rate of combined visits to hematologists did not change from 510.7 [505.1 - 516.4] to 508.7 [500.6 - 516.8] (p = 0.68). The rate of ED visits significantly decreased from 95.1 [92.7 - 97.6] in the pre-pandemic cohort to 84.7 [81.4 - 88.0] in the COVID-19 cohort (p < 0.0001). The rate of unplanned hospitalizations did not change from 64.8 [62.8 - 66.8] to 65.7 [62.9 - 68.7] (p = 0.60). Conclusions: Primary care visits for patients with hematologic malignancies did not significantly change during the pandemic, but there was a sizeable shift from in-person to telemedicine visits. Similar findings were seen for visits to hematologists. While the rate of visits to the ED decreased, potentially due to concern of being exposed to the COVID-19 virus, the shift in ambulatory practices did not seem to impact the rate of unplanned hospitalizations. Disclosures No relevant conflicts of interest to declare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".