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Appropriate Assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression

American journal of epidemiology, 2005-01, Vol.161 (1), p.81-88 [Peer Reviewed Journal]

2005 INIST-CNRS ;Copyright Oxford University Press(England) Jan 01, 2005 ;ISSN: 0002-9262 ;EISSN: 1476-6256 ;DOI: 10.1093/aje/kwi017 ;PMID: 15615918 ;CODEN: AJEPAS

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Title:
Appropriate Assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression
Author: Larsen, Klaus ; Merlo, Juan
Subjects: Aged ; Aged, 80 and over ; Analysis. Health state ; Biological and medical sciences ; data interpretation ; epidemiologic methods ; Epidemiology ; Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi ; General aspects ; Health Sciences ; hierarchical ; hierarchical model ; Hospitalization - statistics & numerical data ; Humans ; Hälsovetenskap ; interval odds ratio ; IOR ; Logistic Models ; Male ; median odds ratio ; Medical and Health Sciences ; Medical sciences ; Medicin och hälsovetenskap ; Middle Aged ; MOR ; Myocardial Ischemia - epidemiology ; odds ratio ; Physicians, Family - utilization ; Public Health, Global Health, Social Medicine and Epidemiology ; Public health. Hygiene ; Public health. Hygiene-occupational medicine ; Regression analysis ; Residence Characteristics ; statistical ; Urban Population
Is Part Of: American journal of epidemiology, 2005-01, Vol.161 (1), p.81-88
Description: The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic regression. For this purpose, the authors propose an odds ratio measure, the interval odds ratio, that takes these difficulties into account. The authors demonstrate the two measures by investigating heterogeneity between neighborhoods and effects of neighborhood-level covariates in two examples—public physician visits and ischemic heart disease hospitalizations—using 1999 data on 11,312 men aged 45–85 years in Malmö, Sweden.
Publisher: Cary, NC: Oxford University Press
Language: English
Identifier: ISSN: 0002-9262
EISSN: 1476-6256
DOI: 10.1093/aje/kwi017
PMID: 15615918
CODEN: AJEPAS
Source: GFMER Free Medical Journals
MEDLINE
Alma/SFX Local Collection
SWEPUB Freely available online

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Appropriate Assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression

2005 INIST-CNRS ;Copyright Oxford University Press(England) Jan 01, 2005 ;ISSN: 0002-9262 ;EISSN: 1476-6256 ;DOI: 10.1093/aje/kwi017 ;PMID: 15615918 ;CODEN: AJEPAS

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