인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Objectives: To identify multidimensional socioeconomic latent classes and their associations with cancer screening participation among Korean adults. Methods: Data from 181,457 adults aged ≥ 40 years were extracted from the 2025 Community Health Survey. Using income, education, occupation, and residential area, a latent class analysis reflecting the survey design’s complexity was conducted. Hierarchical logistic regression was performed, calculating estimated adjusted odds ratios (aOR) and 95% confidence interval (CI) for screening across classes. The primary estimate was adjusted for predisposing factors (age, sex, and residential status with spouse). Results: Six classes were identified, as follows: socially advantaged, economically vulnerable, manual/sales-service labor, highly educated and economically inactive, agricultural, and rural vulnerable. The five non-advantaged classes (vs. socially advantaged) showed lower odds for screening. The economically vulnerable (aOR 0.42, 95% CI 0.39–0.44) and rural vulnerable (aOR 0.49, 95% CI 0.46–0.52) classes showed the lowest likelihood. Despite sharing rural residence with the rural vulnerable class, the agricultural class (aOR 0.65, 95% CI 0.61–0.69) resembled the intermediate classes. A linear gradient was not observed for the disparity, varying by how disadvantages combined. Conclusion: The economically vulnerable and rural vulnerable classes showed the lowest screening likelihood. Tailored interventions tackling co-occurring vulnerabilities are needed.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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