ArticleBMJ open2022
Latent class cluster analysis of knowledge on acute myocardial infarction in community residents: a cross-sectional study in Tianjin, China.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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14 authors at 3 institutions in 1 country.
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Abstract
objectivePublic knowledge of early onset symptoms and risk factors (RF) of acute myocardial infarction (AMI) is very important for prevention, recurrence and guide medical seeking behaviours. This study aimed to identify clusters of knowledge on symptoms and RFs of AMI, compare characteristics and the awareness of the need for prompt treatment.
designMultistage stratified sampling was used in this cross-sectional study. Latent GOLD Statistical Package was used to identify and classify the respondent subtypes of the knowledge on AMI symptoms or modifiable RFs. Multivariable logistic regression was performed to identify factors that predicted high knowledge membership.
participantsA structured questionnaire was used to interview 4200 community residents aged over 35 in China. 4122 valid questionnaires were recovered.
resultsFor AMI symptoms and RFs, the knowledge levels were classified into two or three distinct clusters, respectively. 62.7% (Symptom High Knowledge Cluster) and 39.5% (RF High Knowledge Cluster) of the respondents were able to identify most of the symptoms and modifiable RFs. Respondents who were highly educated, had higher monthly household income, were insured, had regular physical examinations, had a disease history of AMI RFs, had AMI history in immediate family member or acquaintance or had received public education on AMI were observed to have higher probability of knowledge on symptoms and RFs. There was significant difference in awareness of the prompt treatment in case of AMI occurs among different clusters. 'Calling an ambulance' was the most popular option in response of seeing others presenting symptoms of AMI.
conclusionsA moderate or relatively low knowledge on AMI symptoms and modifiable RFs was observed in our study. Identification of Knowledge Clusters could be a way to detect specific targeted groups with low knowledge of AMI, which may facilitate health education, further reduce the prehospital delay in China and improve patient outcomes.
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