Classification of main risk domains in population at high-risk of cardiovascular diseases: a latent class analysis

  Objective  To explore the classification of main risk domains in the population at high-risk of cardiovascular diseases (CVDs) with latent class analysis (LCA) and to provide evidences for making targeted interventions on CVDs in the population.

  Methods  We screened out 2 951 people at high-risk of CVDs among 13 908 permanent residents aged 35 – 75 years recruited using cluster sampling in Chibi city of Hubei province from December 2016 to April 2017. Groups with different main risk domains in the people at high CVDs risk were identified using LCA method and chi-square test was adopted to assess distribution differences in demographic characteristics and disease history among various groups.

  Results  Based on LCA model established, four main risk domain groups were identified among the 2 951 people at high CVDs risk, including those with dyslipidemia (n = 860, 29.1%), abnormal body weight (477, 16.2%), abnormal respiratory function (672, 22.8%), and abnormal respiratory function during sleep (942, 31.9%). There were significant distribution differences in sex, age, and disease history among the four groups (P < 0.01).

  Conclusion  Dyslipidemia, abnormal body weight, abnormal respiratory function and abnormal respiratory function during sleep are main risk domains and dyslipidemia and abnormal respiratory function during sleep are more common among community populations at high-risk of cardiovascular diseases.