The association between patients’ frailty status, multimorbidity, and demographic characteristics and changes in primary care for chronic conditions during the COVID-19 pandemic: a pre-post study
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to assess the impact of SARS-COV-2 (Severe acute respiratory syndrome coronavirus 2) pandemic on primary care management (frequency of monitoring activities, regular prescriptions, and test results) of older adults with common chronic conditions (diabetes, hypertension, and chronic kidney disease) and to examine whether any changes were associated with age, sex, neighbourhood income, multimorbidity, and frailty. METHODS: A research database from a sub-set of McMaster University Sentinel and Information Collaboration family practices was used to identify patients ≥65 years of age with a frailty assessment and 1 or more of the conditions. Patient demographics, chronic conditions, and chronic disease management information were retrieved. Changes from 14 months pre to 14 months since the pandemic were described and associations between patient characteristics and changes in monitoring, prescriptions, and test results were analysed using regression models. RESULTS: The mean age of the 658 patients was 75 years. While the frequency of monitoring activities and prescriptions related to chronic conditions decreased overall, there were no clear trends across sub-groups of age, sex, frailty level, neighbourhood income, or number of conditions. The mean values of disease monitoring parameters (e.g. blood pressure) did not considerably change. The only significant regression model demonstrated that when controlling for all other variables, patients with 2 chronic conditions and those with 4 or more conditions were twice as likely to have reduced numbers of eGFR (Estimated glomerular filtration rate) measures compared to those with only 1 condition ((OR (odds ratio) = 2.40, 95% CI [1.19, 4.87]); (OR = 2.19, 95% CI [1.12, 4.25]), respectively). CONCLUSION: In the first 14 months of the pandemic, the frequency of common elements of chronic condition care did not notably change overall or among higher-risk patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".