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Combining data from primary and ancillary surveys to assess the association between neighborhood-level characteristics and health outcomes: The multi-ethnic study of artherosclerosis
Journal article   Peer reviewed

Combining data from primary and ancillary surveys to assess the association between neighborhood-level characteristics and health outcomes: The multi-ethnic study of artherosclerosis

B. N. Sánchez, T. E. Raghunathan, A. V. Diez Roux, Y. Zhu and O. Lee
Statistics in medicine, v 27(27), pp 5745-5763
29 Nov 2008
PMID: 18693328
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1002/sim.3384View
Published, Version of Record (VoR) Open

Abstract

Ancillary survey Maximum likelihood Social epidemiology
There is increasing interest in understanding the role of neighborhood-level factors on the health of individuals. Many large-scale epidemiological studies that accurately measure health status of individuals and individual risk factors exist. Sometimes these studies are linked to area-level databases (e.g. census) to assess the association between crude area-level characteristics and health. However, information from such databases may not measure the neighborhood-level constructs of interest. More recently, large-scale epidemiological studies have begun collecting data to measure specific features of neighborhoods using ancillary surveys. The ancillary surveys are composed of a separate, typically larger, set of individuals. The challenge is then to combine information from these two surveys to assess the role of neighborhood-level factors. We propose a method for combining information from the two data sources using a likelihood-based framework. We compare it with currently used ad hoc approaches via a simulation study. The simulation study shows that the proposed approach yields estimates with better sampling properties (less bias and better coverage probabilities) compared with the other approaches. However, there are cases where some ad hoc approaches may provide adequate estimates. We also compare the methods by applying them to the Multi-Ethnic Study of Atherosclerosis and its Neighborhood Ancillary Survey.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#10 Reduced Inequalities

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Collaboration types
Domestic collaboration
Web of Science research areas
Mathematical & Computational Biology
Medical Informatics
Medicine, Research & Experimental
Public, Environmental & Occupational Health
Statistics & Probability
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