Socio-spatial Patterns of Neighborhood Effects on Adult Obesity in Taiwan: A Multi-level Model  

Oleh:

Chen, Duan-Rung ; Wen, Tzai-Hung

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Social Science & Medicine (www.elsevier.com/locate/sosscimed) vol. 70 no. 6 (Mar. 2010), page 823-833.
Topik: Body Mass Index (BMI); Socioeconomic Position; Health Inequality; Spatial Dependence; Taiwan; Obesity; Neighborhood

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: SS53 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Obesity, one of the most significant health problems now facing developed countries, has been increasing steadily in Taiwan. This study addresses how neighborhood factors affect individual obesity by simultaneously examining individual-level socioeconomic status and neighborhood-level characteristics using a multi-level approach combined with a spatial analysis. The data are from Taiwan's 2001 Social Development Survey on Health and Safety; a representative sample of 27,593 adults over 262 townships (i.e. neighborhoods). A spatial autocorrelation model is employed to investigate the spatial clustering of neighborhood affluence. A two-level Generalized Hierarchical Linear Model (GHLM) is used to combine neighborhood-level (level-2) characteristics (i.e., spatial patterns of neighborhood affluence and ethnic composition), and individual-level SES position (level-1) to examine the factors associated with adult obesity risk. Three principal findings were obtained. First, individual obesity risk is significantly higher in spatially clustered neighborhoods of economic affluence. Neighborhood factors associated with obesity are likely to operate over a wide geographical area and are not limited to conditions in the immediate residential neighborhood. Second, aboriginal people living adjacent to the most affluent cluster in northern Taiwan have elevated obesity risk, revealing possible spatial diffusion and ethnic acculturation. Third, adult obesity is, however, associated with socioeconomically disadvantaged groups in different neighborhood contexts. These findings suggest that accounting for spatial interdependencies among neighborhoods enhances the accuracy of estimated neighborhood effects on obesity.

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