ArticleArchives of public health = Archives belges de sante publique2021
Interpreting a standardized and normalized measure of neighborhood socioeconomic status for a better understanding of health differences.
Article in Archives of public health = Archives belges de sante publique, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
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- Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.International journal of computer assisted radiology and surgery · 2024Article
- Modeling children's moral development in postwar Taiwan through naturalistic observations preserved in historical texts.Scientific reports · 2024Article
- Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches.AIMS public health · 2024Article
- Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.PeerJ. Computer science · 2024Article
- 3D Video Tracking Technology in the Assessment of Orofacial Impairments in Neurological Disease: Clinical Validation.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Census-Tract-Level Median Household Income and Median Family Income Estimates: A Unidimensional Measure of Neighborhood Socioeconomic Status?International journal of environmental research and public health · 2022Article
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Abstract
backgroundStandardization and normalization of continuous covariates are used to ease the interpretation of regression coefficients. Although these scaling techniques serve different purposes, they are sometimes used interchangeably or confused for one another. Therefore, the objective of this study is to demonstrate how these scaling techniques lead to different interpretations of the regression coefficient in multilevel logistic regression analyses.
methodsArea-based socioeconomic data at the census tract level were obtained from the 2015-2019 American Community Survey for creating two measures of neighborhood socioeconomic status (SES), and a hypothetical data on health condition (favorable versus unfavorable) was constructed to represent 3000 individuals living across 300 census tracts (i.e., neighborhoods). Two measures of neighborhood SES were standardized by subtracting its mean and dividing by its standard deviation (SD) or by dividing by its interquartile range (IQR), and were normalized into a range between 0 and 1. Then, four separate multilevel logistic regression analyses were conducted to assess the association between neighborhood SES and health condition.
resultsBased on standardized measures, the odds of having unfavorable health condition was roughly 1.34 times higher for a one-SD change or a one-IQR change in neighborhood SES; these reflect a health difference of individuals living in relatively high SES (relatively affluent) neighborhoods and those living in relatively low SES (relatively deprived) neighborhoods. On the other hand, when these standardized measures were replaced by its respective normalized measures, the odds of having unfavorable health condition was roughly 3.48 times higher for a full unit change in neighborhood SES; these reflect a health difference of individuals living in highest SES (most affluent) neighborhoods and those living in lowest SES (most deprived) neighborhoods.
conclusionMultilevel logistic regression analyses using standardized and normalized measures of neighborhood SES lead to different interpretations of the effect of neighborhood SES on health. Since both measures are valuable in their own right, interpreting a standardized and normalized measure of neighborhood SES will allow us to gain a more rounded view of the health differences of individuals along the gradient of neighborhood SES in a certain geographic location as well as across different geographic locations.
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