Int J Health Geogr - A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea.

Tópicos

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Resumo

CKGROUND: This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets.METHODS: In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs.RESULTS: The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study.CONCLUSIONS: The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts.

Resumo Limpo

ckground studi aim suggest approach integr multilevel model eigenvector spatial filter method appli case studi selfrat health status south korea mani previous healthrel studi multilevel model singlelevel spatial regress use separ howev two method use conjunct object approach import healthrel analys multilevel model enabl simultan analysi individu neighborhood factor influenc health outcom howev result convent multilevel model potenti mislead spatial depend across neighborhood exist spatial depend healthrel data indic health outcom nearbi neighborhood similar distant neighborhood spatial regress model can address problem model spatial depend studi explor possibl integr multilevel model eigenvector spatial filter advanc spatial regress address spatial depend datasetsmethod spatial filter multilevel model eigenvector function addit explanatori variabl account unexplain spatial depend within neighborhoodlevel error specif address inabl convent multilevel model account spatial depend therebi generat robust outputsresult find show sex employ status month household incom perceiv level stress signific associ selfrat health status resid live neighborhood low depriv high doctortoresid ratio tend report higher health status spatial filter multilevel model provid unbias estim improv explanatori power model compar convent multilevel model although chang sign paramet signific level two model case studyconclus integr approach propos paper use tool understand geograph distribut selfrat health status within multilevel framework futur research use appli spatial filter multilevel model dataset order clarifi differ two model anticip integr method will also outperform convent model use context

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