Understanding the Impact of COVID-10 on Antibiotic User in Canada through the use of Electronic Medical Records
Bibliographic record
Abstract
Context: The COVID-19 pandemic is expected to have a dramatic change in the diagnosis and subsequent antibiotic treatment of respiratory symptoms. Objective: Compare healthcare utilization (interactions, prescriptions) between COVID-19 positive patients and patients with a) Influenza (Flu); b) Respiratory Tract Infection (RTI); and c) COVID-19 negative. Study Design and Analysis: A matched-pair cohort study design was used. Three cohorts were created by matching exposed patients (COVID-19 positive) with three groups of unexposed patients (those with Flu, RTI and COVID-19 negative). Each exposed patient was matched on age, sex, province, and month of visit. Logistic regression modeling was conducted. Dataset: Canadian Primary Care Sentinel Surveillance Network electronic medical record data from sites in British Columbia, Alberta, Manitoba, Ontario, Quebec, Nova Scotia and Newfoundland. Population Studied: Patients that met the case definition criteria for: COVID-19 (April and December 2020), Flu (October 2017 and December 2020), RTI (April and December 2020) or COVID-19 negative (April and December 2020). Outcome measures: Five outcomes were evaluated at four follow-up intervals (30-, 60-, 90- and 180-days of index event): i) antibiotic prescription; ii) non-antibiotic prescriptions; iii) all cause visits; iv) follow-up visits; and v) visits with a bacterial diagnosis. Results: There were a total of 3,073 COVID-19 patients identified in the CPCSSN database during the study period (April to December 2020) that were matched to patients in the three unexposed groups. The three cohorts were more female (58%), and had younger and middle-aged adults (29.4% and 38.9%, respectively) than children and older adults. Most patients lived in Alberta, Ontario, or British Columbia. There were significantly more urban patients with COVID-19 than with Flu, RTI or non-COVID. Patients with COVID-19 were significantly less likely to receive an antibiotic prescription than patients with flu (OR=0.20, 95% CI (0.14, 0.29)), RTI (OR=0.08, 95% CI (0.06, 0.12)) or patients without COVID-19 (OR=0.64, 95% CI (0.44, 0.94)). Patients with COVID-19 were significantly more likely to have at least one one visit within 30 days of their index event, compared to patients with RTI (OR=2.23, 95% CI (1.98, 2.51)) or patients without COVID-19 (OR=3.87, 95% CI (3.39, 4.41)). Conclusions: Primary care data are a valuable resource to further understand the epidemiology of COVID-19.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".