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P74 Clustering of housing disadvantages and association with quality of life among Australian children
  1. Yuxi Li1,
  2. Ankur Singh2,
  3. Rebecca Bentley1
  1. 1Centre for Health Policy, School of Population and Global Health, The University of Melbourne, Melbourne, Australia
  2. 2Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia


Background The impact of housing as a social determinant of health on children’s health outcomes has been widely documented. However, research attention is often focussed on separate components of housing, while in reality, housing disadvantages are likely to co-occur, in that children who have one disadvantage are much more likely to report another. A better understanding of the patterning of housing disadvantages would inform targeted interventions that could potentially benefit children’s health across the life course. In this study, we aim to 1) examine a clustering pattern of housing disadvantages; 2) investigate the association between this pattern and paediatric quality of life among Australian children.

Methods Data on 5107 children of the Longitudinal Study of Australian Children (LSAC) birth cohort are used. LSAC is a national representative longitudinal study of Australian children’s growth and development. This analysis is based on nine biennial waves of data from the Birth cohort of LSAC children aged less than 12 months in 2003–2004. Ten housing disadvantages from wave 1 are examined: dwelling type, dwelling condition, presence of noise, number of house-moves, number of bedrooms, unable to pay rent of mortgage, unable to heat or cool home, housing tenure, neighbourhood facilities, neighbourhood liveability. Quality of life (wave 9) was measured the 23-item Paediatric Quality of Life Inventory (PedsQL). Factor analysis and latent class analysis are used to identify groups of clustering, followed by Bayesian 2-level logistic regressions to investigate the relationships between clusters and quality of life.

Results (Please note the analysis is ongoing and these are preliminary results for aim 1)

Four clusters were identified from factor analysis: 1) ‘Financial factor’: unable to heat or cool housing (factor loading=0.56), unable to pay rent or mortgage (FL=0.55). 2) ‘Quality factor’: housing condition (FL=0.45), number of bedrooms (FL=0.41), noise (FL=0.40). 3) ‘Stability factor’: number of house-moves (FL=0.52), dwelling type (FL=0.35), tenure (FL=0.57). 4) ‘Neighbourhood factor’: neighbourhood facilities (FL=0.54), neighbourhood liveability (FL=0.52). Results from latent class analysis confirmed a similar pattern.

Conclusion (Please note the analysis is ongoing and the discussion is only based on preliminary results)

These findings demonstrate that housing disadvantages cluster in specific patterns. Further investigations are underway to examine the associations between the four clusters and quality of life, and whether gender modifies the associations.

  • Housing
  • Child health
  • Clustering

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