National Trends in the Prevalence of Unmet Health Care and Dental Care Needs During the COVID-19 Pandemic: Longitudinal Study in South Korea, 2009-2022
Bibliographic record
Abstract
Background Although previous studies have investigated trends in unmet health care and dental care needs, most have focused on specific groups, such as patients with chronic conditions and older adults, and have been limited by smaller data sets. Objective This study aims to investigate the trends and relative risk factors for unmet health care and dental care needs, as well as the impact of the COVID-19 pandemic on these needs. Methods We assessed unmet health care and dental care needs from 2009 to 2022 using data from the Korea Community Health Survey (KCHS). Our analysis included responses from 2,750,212 individuals. Unmet health care or dental care needs were defined as instances of not receiving medical or dental services deemed necessary by experts or desired by patients. Results From 2009 to 2022, the study included 2,700,705 individuals (1,229,671 men, 45.53%; 673,780, 24.95%, aged 19-39 years). Unmet health care needs decreased before the COVID-19 pandemic; however, during the pandemic, there was a noticeable increase (βdiff 0.10, 95% CI 0.09-0.11). Unmet dental care needs declined before the pandemic and continued to decrease during the pandemic (βdiff 0.23, 95% CI 0.22-0.24). Overall, the prevalence of unmet dental care needs was significantly higher than that for unmet health care needs. While the prevalence of unmet health care needs generally decreased over time, the β difference during the pandemic increased compared with prepandemic values. Conclusions Our study is the first to analyze national unmet health care and dental care needs in South Korea using nationally representative, long-term, and large-scale data from the KCHS. We found that while unmet health care needs decreased during COVID-19, the decline was slower compared with previous periods. This suggests a need for more targeted interventions to prevent unmet health care and dental care needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".