Analysis of Emergency Department Encounters Among High Users of Health Care and Social Service Systems Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: Although the general US population had fewer emergency department (ED) visits during the COVID-19 pandemic, patterns of use among high users are unknown. Objectives: To examine natural trends in ED visits among high users of health and social services during an extended period and assess whether these trends differed during COVID-19. Design, Setting, and Participants: This retrospective cohort study combined data from 9 unique cohorts, 1 for each fiscal year (July 1 to June 30) from 2012 to 2021, and used mixed-effects, negative binomial regression to model ED visits over time and assess ED use among the top 5% of high users of multiple systems during COVID-19. Data were obtained from the Coordinated Care Management System, a San Francisco Department of Public Health platform that integrates medical and social information with service use. Exposures: Fiscal year 2020 was defined as the COVID-19 year. Main Outcomes and Measures: Measured variables were age, gender, language, race and ethnicity, homelessness, insurance status, jail health encounters, mental health and substance use diagnoses, and mortality. The main outcome was annual mean ED visit counts. Incidence rate ratios (IRRs) were used to describe changes in ED visit rates both over time and in COVID-19 vs non-COVID-19 years. Results: Of the 8967 participants, 3289 (36.7%) identified as White, 3005 (33.5%) as Black, and 1513 (16.9%) as Latinx; and 7932 (88.5%) preferred English. The mean (SD) age was 46.7 (14.2) years, 6071 (67.7%) identified as men, and 7042 (78.5%) had experienced homelessness. A statistically significant decrease was found in annual mean ED visits among high users for every year of follow-up until year 8, with the largest decrease occurring in the first year of follow-up (IRR, 0.41; 95% CI, 0.40-0.43). However, during the pandemic, ED visits decreased 25% beyond the mean reduction seen in prepandemic years (IRR, 0.75; 95% CI, 0.72-0.79). Conclusions and Relevance: In this study, multiple cohorts of the top 5% of high users of multiple health care systems in San Francisco had sustained annual decreases in ED visits from 2012 to 2021, with significantly greater decreases during COVID-19. Further research is needed to elucidate pandemic-specific factors associated with these findings and understand how this change in use was associated with health outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".