Trends in Avoidable Hospitalizations Before and During the COVID-19 Pandemic: Multiple Cross-Sectional Study Using Administrative Data From Beijing, China
Bibliographic record
Abstract
Background: Avoidable hospitalizations (AHs) have been widely used in high-income countries as a proxy indicator for the quality of primary care. However, it is rarely evaluated in low- and middle-income countries such as China. Studies examining changes in AHs before and during the COVID-19 pandemic are also limited. The appropriateness of AHs as an indicator measuring primary care quality under pandemic conditions has not been well discussed. Objective: This study aims to describe trends in AHs in Beijing, China, during both the prepandemic (2016-2019) and pandemic (2020-2021) periods and examine factors associated with AH rates. Methods: We used hospital discharge data of Beijing residents between January 1, 2016, and December 31, 2021. We identified AH cases from all discharge cases and calculated AH rates each year, adjusting for population structure changes. We performed regression analyses to explore factors associated with AH rates, where the COVID-19 outbreak, health care resources, and socioeconomic characteristics were used as the main explanatory variables. Results: Before the COVID-19 pandemic, the total number of hospital discharges in Beijing increased steadily from 2016 to 2019 but decreased sharply in 2020 and partially rebounded in 2021. The sex- and age-standardized AH rate per 100,000 population rose from 514.7 (95% CI 511.4-517.9) in 2016 to 552.8 (95% CI 549.4-556.1) in 2019. Then it declined to 331.2 (95% CI 328.6-333.8) in 2020 and rebounded to 465.1 (95% CI 462.1-468.1) in 2021, which was still below the prepandemic level. Regression analyses show that the presence of newly confirmed COVID-19 cases was significantly associated with a lower AH rate. As for other factors, higher densities of primary physicians were linked to lower AH rates. Moreover, AH rates were also associated with population structure, the level of economic development, and demographic variables. Conclusions: The AH rate in Beijing exhibited a consistent upward trend before the pandemic and remained higher than in many high-income countries. These characteristics suggest a potential overuse of tertiary care and highlight the necessity for health care system reforms in Beijing, particularly a transition from the hospital-centered model to a primary care-focused delivery system. In addition, the observed associations between AH rates and factors, such as pandemic shock and socioeconomic variables, indicate that AH should be interpreted with appropriate controls when it is used as an indicator of primary care performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".