BMC public health · 2026
Prevalence and correlates of cardiovascular prevention gaps among adults with cardio-kidney-metabolic risk: A hospital-based cross-sectional study.
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Nawsherwan, Yousaf Tanzeel, Ali Sardar, Bashir Musa Bin
Аннотация
BACKGROUND: Cardiovascular prevention gaps may arise when adults with cardio-kidney-metabolic (CKM) risk have inadequate risk awareness, screening, or preventive-care engagement. Evidence integrating clinical risk burden with digital and artificial-intelligence (AI) health-literacy indicators is limited. We assessed the prevalence and correlates of cardiovascular prevention gaps in a hospital-based sample of Chinese adults, hypothesizing that gaps would be common and would cluster with greater CKM burden.
METHODS: In this hospital-based analytical cross-sectional study, 542 adults attending Zhongnan Hospital of Wuhan University (January-April 2026) were recruited by convenience sampling from 700 invited (563 returned or initiated the survey; 21 incomplete responses excluded). The primary outcome, cardiovascular prevention gap, required at least one cardiovascular/CKM risk indicator together with at least one prevention-related deficit in risk awareness, recent screening, perceived risk, regular-care access, or care barriers. Characteristics were compared by gap status. A non-circular multivariable logistic-regression model excluded the CVD risk-factor count, regular-clinic access, and cost barrier; because current smoking showed near-complete separation, the model was estimated with Firth penalisation. Analyses were exploratory.
RESULTS: Overall, 377 of 542 participants had a cardiovascular prevention gap (prevalence 69.6%). The most frequent components were lack of lipid-status awareness (55.5%), no regular clinic access (52.0%), and no lipid screening within 12 months (50.2%). Participants with gaps were older (41.4 vs. 36.0 years; P < 0.001), more often male (53.8% vs. 41.2%) and current smokers (25.2% vs. 1.8%), and had higher CKM burden (high CKM risk 82.2% vs. 15.2%). In the adjusted non-circular model, older age was independently associated with prevention gap (adjusted OR 1.04 per year, 95% CI 1.03-1.07). Current smoking showed a strong apparent association (Firth-penalized OR 20.9, 95% CI 7.1-62.1) that largely reflected its role in the risk-eligibility definition and attenuated when smoking was excluded as a qualifying risk factor. eHealth literacy, AI health-literacy, digital-health use, and other sociodemographic and healthcare-access factors were not independently associated.
CONCLUSIONS: Cardiovascular prevention gaps were common and clustered with adverse CKM profiles and were concentrated among older adults. These exploratory, single-center findings support integrated prevention that combines risk awareness, routine screening, accessible preventive care, and CKM risk identification, with particular attention to older adults.
METHODS: In this hospital-based analytical cross-sectional study, 542 adults attending Zhongnan Hospital of Wuhan University (January-April 2026) were recruited by convenience sampling from 700 invited (563 returned or initiated the survey; 21 incomplete responses excluded). The primary outcome, cardiovascular prevention gap, required at least one cardiovascular/CKM risk indicator together with at least one prevention-related deficit in risk awareness, recent screening, perceived risk, regular-care access, or care barriers. Characteristics were compared by gap status. A non-circular multivariable logistic-regression model excluded the CVD risk-factor count, regular-clinic access, and cost barrier; because current smoking showed near-complete separation, the model was estimated with Firth penalisation. Analyses were exploratory.
RESULTS: Overall, 377 of 542 participants had a cardiovascular prevention gap (prevalence 69.6%). The most frequent components were lack of lipid-status awareness (55.5%), no regular clinic access (52.0%), and no lipid screening within 12 months (50.2%). Participants with gaps were older (41.4 vs. 36.0 years; P < 0.001), more often male (53.8% vs. 41.2%) and current smokers (25.2% vs. 1.8%), and had higher CKM burden (high CKM risk 82.2% vs. 15.2%). In the adjusted non-circular model, older age was independently associated with prevention gap (adjusted OR 1.04 per year, 95% CI 1.03-1.07). Current smoking showed a strong apparent association (Firth-penalized OR 20.9, 95% CI 7.1-62.1) that largely reflected its role in the risk-eligibility definition and attenuated when smoking was excluded as a qualifying risk factor. eHealth literacy, AI health-literacy, digital-health use, and other sociodemographic and healthcare-access factors were not independently associated.
CONCLUSIONS: Cardiovascular prevention gaps were common and clustered with adverse CKM profiles and were concentrated among older adults. These exploratory, single-center findings support integrated prevention that combines risk awareness, routine screening, accessible preventive care, and CKM risk identification, with particular attention to older adults.