Juan Merlo1, Basile Chaix, Min Yang, John Lynch, Lennart Råstam. 1. Department of Community Medicine (Section of Preventive Medicine), Malmö University Hospital, Faculty of Medicine (Campus Malmö), Lund University, S-205 02 Malmö, Sweden. juan.merlo@med.lu.se
Abstract
STUDY OBJECTIVE: Using a conceptual rather than a mathematical approach, this article proposed a link between multilevel regression analysis (MLRA) and social epidemiological concepts. It has been previously explained that the concept of clustering of individual health status within neighbourhoods is useful for operationalising contextual phenomena in social epidemiology. It has been shown that MLRA permits investigating neighbourhood disparities in health without considering any particular neighbourhood characteristic but only information on the neighbourhood to which each person belongs. This article illustrates how to analyse cross level (neighbourhood-individual) interactions, how to investigate associations between neighbourhood characteristics and individual health, and how to use the concept of clustering when interpreting those associations and geographical differences in health. DESIGN AND PARTICIPANTS: A MLRA was performed using hypothetical data pertaining to systolic blood pressure (SBP) from 25 000 subjects living in the 39 neighbourhoods of an imaginary city. Associations between individual characteristics (age, body mass index (BMI), use of antihypertensive drug, income) or neighbourhood characteristic (neighbourhood income) and SBP were analysed. RESULTS: About 8% of the individual differences in SBP were located at the neighbourhood level. SBP disparities and clustering of individual SBP within neighbourhoods increased along individual BMI. Neighbourhood low income was associated with increased SBP over and above the effect of individual characteristics, and explained 22% of the neighbourhood differences in SBP among people of normal BMI. This neighbourhood income effect was more intense in overweight people. CONCLUSIONS: Measures of variance are relevant to understanding geographical and individual disparities in health, and complement the information conveyed by measures of association between neighbourhood characteristics and health.
STUDY OBJECTIVE: Using a conceptual rather than a mathematical approach, this article proposed a link between multilevel regression analysis (MLRA) and social epidemiological concepts. It has been previously explained that the concept of clustering of individual health status within neighbourhoods is useful for operationalising contextual phenomena in social epidemiology. It has been shown that MLRA permits investigating neighbourhood disparities in health without considering any particular neighbourhood characteristic but only information on the neighbourhood to which each person belongs. This article illustrates how to analyse cross level (neighbourhood-individual) interactions, how to investigate associations between neighbourhood characteristics and individual health, and how to use the concept of clustering when interpreting those associations and geographical differences in health. DESIGN AND PARTICIPANTS: A MLRA was performed using hypothetical data pertaining to systolic blood pressure (SBP) from 25 000 subjects living in the 39 neighbourhoods of an imaginary city. Associations between individual characteristics (age, body mass index (BMI), use of antihypertensive drug, income) or neighbourhood characteristic (neighbourhood income) and SBP were analysed. RESULTS: About 8% of the individual differences in SBP were located at the neighbourhood level. SBP disparities and clustering of individual SBP within neighbourhoods increased along individual BMI. Neighbourhood low income was associated with increased SBP over and above the effect of individual characteristics, and explained 22% of the neighbourhood differences in SBP among people of normal BMI. This neighbourhood income effect was more intense in overweight people. CONCLUSIONS: Measures of variance are relevant to understanding geographical and individual disparities in health, and complement the information conveyed by measures of association between neighbourhood characteristics and health.
Authors: J Merlo; P O Ostergren; O Hagberg; M Lindström; A Lindgren; A Melander; L Råstam; G Berglund Journal: J Epidemiol Community Health Date: 2001-11 Impact factor: 3.710
Authors: H Ohlsson; U Lindblad; T Lithman; B Ericsson; U-G Gerdtham; A Melander; L Råstam; J Merlo Journal: Eur J Clin Pharmacol Date: 2005-10-19 Impact factor: 2.953
Authors: Pernille Due; Juan Merlo; Yossi Harel-Fisch; Mogens Trab Damsgaard; Bjørn E Holstein; Jørn Hetland; Candace Currie; Saoirse Nic Gabhainn; Margarida Gaspar de Matos; John Lynch Journal: Am J Public Health Date: 2009-03-19 Impact factor: 9.308
Authors: Sherry Baron; Raymond Sinclair; Devon Payne-Sturges; Jerry Phelps; Harold Zenick; Gwen W Collman; Liam R O'Fallon Journal: Am J Public Health Date: 2009-11 Impact factor: 9.308
Authors: Gina S Lovasi; James W Quinn; Virginia A Rauh; Frederica P Perera; Howard F Andrews; Robin Garfinkel; Lori Hoepner; Robin Whyatt; Andrew Rundle Journal: Am J Public Health Date: 2010-03-18 Impact factor: 9.308
Authors: Juan Merlo; Basile Chaix; Henrik Ohlsson; Anders Beckman; Kristina Johnell; Per Hjerpe; L Råstam; K Larsen Journal: J Epidemiol Community Health Date: 2006-04 Impact factor: 3.710