Trends in Outpatient Medical-Care Seeking for Acute Gastroenteritis During the COVID-19 Pandemic, 2020
Bibliographic record
Abstract
The rate of enteric infections reported to public health surveillance decreased during 2020 amid the coronavirus disease 2019 (COVID-19) pandemic. Changes in medical care-seeking behaviors may have impacted the diagnosis of enteric infections contributing to these declines. We examined trends in outpatient medical care-seeking behavior for acute gastroenteritis (AGE) in Colorado during 2020 compared with the that of previous 3 years using electronic health record data from the Colorado Health Observation Regional Data Service (CHORDS). Outpatient medical encounters for AGE were identified using diagnoses codes from the International Classification of Diseases 10th Revision and aggregated by year, quarter, age group, and encounter type. The rate of encounters was calculated by dividing the number of AGE encounters by the corresponding total number of encounters. There were 9064 AGE encounters in 2020 compared with an annual average of 18,784 from 2017 to 2019 (p < 0.01), representing a 52% decrease. The rate of AGE encounters declined after the first quarter of 2020 and remained significantly lower for the rest of the year. Moreover, previously observed trends, including seasonal patterns and the preponderance of pediatric encounters, were no longer evident. Telemedicine modalities accounted for 23% of all AGE encounters in 2020. AGE outpatient encounters in Colorado in 2020 were substantially lower than during the previous 3 years. Decreases remained stable over the second, third, and fourth quarters of 2020 (April–December) and were especially pronounced for children <18 years of age. Changes in medical care-seeking behavior likely contributed to declines in the number of enteric disease cases and outbreaks reported to public health. It is unclear to what extent people were ill with AGE and did not seek medical care because of concerns about the infection risk during a health care visit or to what extent there were reductions in certain exposures and opportunities for disease transmission resulting in less illness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".