Changes in Healthcare Utilization During the COVID-19 Pandemic and Potential Causes—A Cohort Study From Switzerland
Bibliographic record
Abstract
Objectives: To describe the frequency of and reasons for changes in healthcare utilization in those requiring ongoing treatment, and to assess characteristics associated with change, during the second wave of the pandemic. Methods: Corona Immunitas e-cohort study (age ≥20 years) participants completed monthly questionnaires. We compared participants reporting a change in healthcare utilization with those who did not using descriptive and bivariate statistics. We explored characteristics associated with the number of changes using negative binomial regression. Results: The study included 3,190 participants from nine research sites. One-fifth reported requiring regular treatment. Among these, 14% reported a change in healthcare utilization, defined as events in which participants reported that they changed their ongoing treatment, irrespective of the reason. Reasons for change were medication changes and side-effects, specifically for hypertension, or pulmonary embolism treatment. Females were more likely to report changes [Incidence Rate Ratio (IRR) = 2.15, p = 0.002]. Those with hypertension were least likely to report changes [IRR = 0.35, p = 0.019]. Conclusion: Few of those requiring regular treatment reported changes in healthcare utilization. Continuity of care for females and chronic diseases besides hypertension must be emphasized.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".