Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties.
Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted...
| Publicado en: | Journal of Urban Health Vol. 99; no. 3; pp. 457 - 469 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157414101&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157414101 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10993460 GMF jtl: Journal of Urban Health issn: 10993460 maglogo: N pubinfo: dt: Jun2022 vid: 99 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 157414101 10.1007/s11524-022-00632-8 ppf: 457 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties. aug: au: De Silva, S. Shanika A. Meeker, Melissa A. Ryan, Victoria Algur, Yasemin Long, D. Leann Carson, April. P. Black, Nyesha McClure, Leslie A. affil: Department of Epidemiology & Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA Noire Analytics, Birmingham, AL, USA su: Midwest (U.S.) Centers for Disease Control & Prevention (U.S.) Socioeconomic status Neighborhoods Principal components analysis Spatial variation Counties sug: subj: Socioeconomic status Neighborhoods Midwest (U.S.) Centers for Disease Control & Prevention (U.S.) Administration of Public Health Programs Principal components analysis Spatial variation Counties keyword: Geographically weighted principal component analysis Neighborhood SES Principal component analysis Geographically weighted principal component analysis Neighborhood SES Principal component analysis ab: Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted principal component analysis (GWPCA), respectively. We assessed spatial variation in the loadings using a Monte Carlo randomization test. The results indicated that there was statistically significant variation (p = 0.004) in the loadings of the spatial index. The variability of the census variables explained by the spatial index ranged from 60 to 90%. We found that the first geographically weighted principal component explained the most variability in the census variables in counties in the Northeast and the West, and the least variability in counties in the Midwest. We also tested the two measures by assessing the associations with county-level diabetes prevalence using data from the CDC's US Diabetes Surveillance System. While associations of the two NSES measures with diabetes did not differ for this application, the descriptive results suggest that it might be important to consider a spatial index over a global index when constructing national county measures of NSES. The spatial approach may be useful in identifying what factors drive the socioeconomic status of a county and how they vary across counties. Furthermore, we offer suggestions on how a GWPCA–based NSES index may be replicated for smaller geographic scopes. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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