Clustering of multiple health risk factors among a sample of adolescents in Liberia: a latent class analysis.

Aim: Non-communicable diseases (NCDs) are associated with modifiable health risk factors. There is a lack of evidence regarding clusters of health-related behaviours among school-going adolescents from sub-Saharan Africa. This study was conducted to identify clustering patterns of health risk factor...

Full description

Bibliographic Details
Published in:Journal of Public Health: From Theory to Practice (2198-1833) Vol. 30; no. 6; pp. 1389 - 1398
Main Authors: Atorkey, Prince, Asante, Kwaku Oppong
Format: Article
Published: Springer Nature Jun2022
Subjects:
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=156858675&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 156858675
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        21981833
        NENI
      jtl: Journal of Public Health: From Theory to Practice (2198-1833)
      issn: 21981833
      maglogo: N
    pubinfo:
      dt: Jun2022
      vid: 30
      iid: 6
      pid: 237
      pub: Springer Nature
    artinfo:
      ui:
        156858675
        10.1007/s10389-020-01465-y
      ppf: 1389
      ppct: 9
      formats:
      tig:
        atl: Clustering of multiple health risk factors among a sample of adolescents in Liberia: a latent class analysis.
      aug:
        au:
          Atorkey, Prince
          Asante, Kwaku Oppong
        affil:
          School of Medicine and Public Health, University of Newcastle, University Drive, 2308, Callaghan, New South Wales, Australia
          Hunter New England Population Health, Hunter New England Local Health District, Locked Mail Bag 10, 2287, Wallsend, New South Wales, Australia
          Priority Research Centre for Health Behaviour, University of Newcastle, Callaghan, New South Wales, Australia
          Hunter Medical Research Institute, Newcastle, New South Wales, Australia
          Department of Psychology, University of Ghana, Legon, Accra, Ghana
          Department of Psychology, University of the Free State, Bloemfontein, South Africa
      su:
        Liberia
        Risk-taking behavior
        Health behavior
        Sociodemographic factors
        Risk assessment
        Cluster analysis (Statistics)
        Statistical sampling
      sug:
        subj:
          Risk-taking behavior
          Health behavior
          Sociodemographic factors
          Liberia
          Marketing Research and Public Opinion Polling
          Risk assessment
          Cluster analysis (Statistics)
          Statistical sampling
      keyword:
        Adolescents
        Cluster analysis
        Multiple health risk factors
        Adolescents
        Cluster analysis
        Multiple health risk factors
      ab: Aim: Non-communicable diseases (NCDs) are associated with modifiable health risk factors. There is a lack of evidence regarding clusters of health-related behaviours among school-going adolescents from sub-Saharan Africa. This study was conducted to identify clustering patterns of health risk factors (i.e. smoking tobacco, inadequate fruit intake, inadequate vegetable intake, physical inactivity, sedentary behaviour, anxiety and depression) and association with sociodemographic factors among school-going adolescents in Liberia. Subject and methods: The 2017 Liberian Global School-based Student Health Survey on 2774 adolescents aged 11 years and above (52.5% females) sampled with a two-stage cluster sample design was used. Latent class analysis was used to generate the clusters and latent class regression assessed the associations between sociodemographic factors and the clusters. Results: We identified three clusters labelled as (1) 'low substance use, moderately active cluster' (34.8%); (2) 'inadequate fruit and vegetable cluster' (48.9%) and (3) 'risk taking cluster' (16.3%)'. Compared to cluster 1, adolescent boys [AOR = 1.71, 1.29–2.27, p < 0.001], and those in grade 10–12 [AOR = 1.51, 1.13–2.02, p < 0.001] were more likely to belong to cluster 2. Participants aged 15 years and above [AOR = 0.60, 0.39–0.91, p = 0.018] were less likely to belong to cluster 2. Compared to cluster 1, adolescents aged 15 years and above [AOR = 3.58, 1.33–9.62, p = 0.011] and those with low socio-economic status [AOR = 1.83, 1.22–2.73, p = 0.003] were more likely to belong to cluster 3. Conclusion: These results underscore the need for interventions that address adolescent multiple health risk factors, especially considering sociodemographic differences.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N