The Effects of the Health System Response to the COVID-19 Pandemic on Chronic Disease Management: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Individuals with chronic conditions require ongoing disease management to reduce risks of adverse health outcomes. During the COVID-19 pandemic, health care for non-COVID-19 cases was affected due to the reallocation of resources towards urgent care for COVID-19 patients, resulting in inadequate ongoing care for chronic conditions. METHODS: A keyword search was conducted in PubMed, Google Scholar, Science Direct, and Scopus for English language articles published between January 2020 and January 2021. FINDINGS: During the COVID-19 pandemic, in-person care for individuals with chronic conditions have decreased due to government restriction of elective and non-urgent healthcare visits, greater instilled fear over potential COVID-19 exposure during in-person visits, and higher utilization rates of telemedicine compared to the pre-COVID-19 period. Potential benefits of a virtual-care framework during the pandemic include more effective routine disease monitoring, improved patient satisfaction, and increased treatment compliance and follow-up rates. However, more needs to be done to ensure timely and effective access to telemedicine, particularly for individuals with lower digital literacy. Capitation primary care models have been proposed as a more financially-robust approach during the COVID-19 pandemic than fee-for-service primary care models; however, the interplay between different primary models and the health outcomes is still poorly understood and warrants further investigation. Shortages of medication used to manage chronic conditions were also observed at the beginning of the COVID-19 pandemic due to global supply chain disruptions. Finally, patients with chronic conditions faced lifestyle disruptions due to the COVID-19 pandemic, specifically in physical activity, sleep, stress, and mental health, which need to be better addressed. INTERPRETATION: Overall, this review elucidates the disproportionately greater barriers to primary and specialty care that patients with chronic diseases face during the COVID-19 pandemic and emphasizes the urgent need for better chronic disease management strategies moving forward.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".