Latent class cluster analysis of knowledge on acute myocardial infarction symptoms and risk factors in community residents. (14th October 2021)

• Record Type: Journal Article
• Authors: Liu, Y; Ma, J; Zhang, N; Xiao, J.-Y; Wang, J.-X; Li, X.-W; Wang, J; Zhang, Y; Gao, M.-D; Zhang, X; Wang, Y; Wang, J.-X; Xu, S.-B; Gao, J
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Public Health; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.3134
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Knowledge of Acute Myocardial Infarction (AMI) symptoms and risk factors (RF) have been studied worldwide in various populations. However, the studied specific symptom and RF of different surveys varies, the number of surveyed symptom and RF varies, and the criteria for judging good/high knowledge and bad/low knowledge are different (group classification criteria include the number, the percentages or the scores), which makes the results difficult to compare. Purpose: The aim was to identify clusters of knowledge on symptoms and modifiable RFs using Latent Cluster Analysis, and to further compare characteristics and the awareness of the need for prompt treatment among different clusters. Methods: A structured questionnaire was used to interview 4122 community residents over 35 years old on their knowledge about AMI symptoms and RFs. Latent GOLD Statistical Package was used to identify and classify the respondent subtypes (latent classes) of the knowledge on AMI symptoms or modifiable RFs. An individual's class membership probabilities are computed from the estimated model parameters and the observed scores. The multivariable logistic regression was performed in order to identify factors that predicted High knowledge membership. Results: Two and three distinct clusters were identified in terms of knowledge of symptoms and RFs. 62.7% (Symptom High Knowledge Cluster) and 39.5% (RF High Knowledge Cluster) of the respondents were able to identify most symptoms and modifiable RFs. A number of common differences were observed with respect to higher probability of having high knowledge on symptom and RF, which was mostly better among those with higher education and average monthly income, insured, having annual physical examination, have disease history of AMI RFs, AMI history in immediate family member or acquaintance and have been exposed to AMI related public education. There was also significant difference in awareness of the prompt treatment in case of AMI occurs among different symptom or RF clusters. Conclusions: A moderate or relatively low knowledge regarding AMI symptoms and modifiable RFs was observed in our study. Identification of Symptom or RF knowledge clusters can be a way to detect specific targeted groups that are most likely to possess lower knowledge of AMI with readily identifiable characteristics, which may facilitate health education and further reduce the pre-hospital delay and improve patient outcomes. FUNDunding Acknowledgement: Type of funding sources: Foundation. Main funding source(s): Key Project of Scientific and Technological Support Plan of Tianjin in 2020